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INTRODUCTION .......................................................................................................................... 5

1.1 1.2 1.3 1.4 1.5 1.6 2

PROCESS INNOVATION AT NOVOZYMES ..................................................................................... 6 RESEARCH QUESTION .................................................................................................................. 8 SPECIFICATION OF THE RESEARCH QUESTION .......................................................................... 8 MOT PERSPECTIVE .................................................................................................................... 13 PURPOSE AND TARGET GROUPS ................................................................................................. 14 STRUCTURE AND OVERVIEW ...................................................................................................... 14 METHODOLOGY ....................................................................................................................... 16

2.1 THEORETICAL CLARIFICATION ................................................................................................. 16 2.2 THEORETICAL APPLICATION ..................................................................................................... 18 2.3 THE CASE STUDY......................................................................................................................... 21 2.3.1 THE CASE STUDY AS METHODOLOGY ........................................................................................ 21 2.3.2 QUALITATIVE VS. QUANTITATIVE RESEARCH ........................................................................... 21 2.3.3 NUMBER OF INTERVIEWEES ...................................................................................................... 22 2.3.4 INDIVIDUAL OR WORKSHOP INTERVIEWS.................................................................................. 22 2.3.5 PRACTICAL EXECUTION ............................................................................................................ 23 3

INTRODUCTION TO COMPETENCE THEORY.................................................................. 27

3.1 3.2 3.3 3.4 3.4.1 3.4.2 3.4.3 4

TERMINOLOGICAL EUPHORIA ................................................................................................... 27 OUR FOCUS .................................................................................................................................. 29 THE CONSTITUENTS OF COMPETENCIES ................................................................................... 31 ELABORATING ON THE WORKING DEFINITION ....................................................................... 34 HUMAN SKILL ........................................................................................................................... 35 TECHNOLOGY ............................................................................................................................ 39 ORGANISATION ......................................................................................................................... 39

DISCUSSION OF FRAMEWORKS TO IDENTIFY COMPETENCIES .............................. 42

4.1 4.1.1 4.1.2 4.1.3 4.1.4 4.1.5 4.2 4.2.1 4.2.2 4.2.3 4.3 4.3.1 4.3.2 4.3.3 4.4 4.4.1 4.4.2

KLEIN AND HISCOCKS (1994) .................................................................................................... 43 THE METHODOLOGY - SKILL MAPPING ...................................................................................... 43 STRENGTHS AND WEAKNESSES OF THE METHODOLOGY ........................................................... 43 THE METHODOLOGY – SKILL CLUSTERS ................................................................................... 45 STRENGTHS AND WEAKNESSES OF THE METHODOLOGY ........................................................... 45 SUM-UP OF VALUABLE ELEMENTS ............................................................................................ 46 LEWIS AND GREGORY (1996)..................................................................................................... 46 THE METHODOLOGY ................................................................................................................. 46 STRENGTHS AND WEAKNESSES OF THE METHODOLOGY ........................................................... 47 SUM-UP OF VALUABLE ELEMENTS ............................................................................................ 48 DREJER (2002) ............................................................................................................................ 49 THE METHODOLOGY ................................................................................................................. 49 BENEFITS AND WEAKNESSES OF THE METHODOLOGY .............................................................. 49 SUM-UP OF VALUABLE ELEMENTS ............................................................................................ 51 GRIFFITH AND BOISOT (2000).................................................................................................... 51 THE METHODOLOGY ................................................................................................................. 52 BENEFITS AND WEAKNESSES OF THE METHODOLOGY .............................................................. 53

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4.4.3 5

DEVELOPING A METHODOLOGY AND FRAMEWORK ................................................. 56

5.1 5.2 5.3 5.4 5.4.1 5.4.2 5.4.3 5.4.4 5.4.5 6

THE PROCESS ILLUSTRATION .................................................................................................... 56 THE WORKSHOP.......................................................................................................................... 59 OTHER CONSIDERATIONS .......................................................................................................... 60 THE METHODOLOGY AND FRAMEWORK .................................................................................. 60 PHASE 1: PRE-WORKSHOP PREPARATION .................................................................................. 61 PHASE 2: COMMON UNDERSTANDING ....................................................................................... 62 PHASE 3: IDENTIFICATION ......................................................................................................... 63 PHASE 4: SELECTION AND PRIORITISATION .............................................................................. 64 PHASE 5: REVIEW PROCESS ....................................................................................................... 64

EMPIRICAL CASE PRESENTATION ..................................................................................... 65

6.1 6.2 6.3 6.3.1 6.3.2 6.4 7

SUM-UP OF VALUABLE ELEMENTS ............................................................................................ 54

NOVOZYMES A/S ........................................................................................................................ 65 PROTEASE DISCOVERY DEPARTMENT ....................................................................................... 67 PROTEASE SCREENING PROCESS ............................................................................................... 68 SCREENING PROCESS IN 1999.................................................................................................... 69 SCREENING PROCESS 2004........................................................................................................ 71 CORE COMPETENCE OF PROTEASE DISCOVERY DEPARTMENT .............................................. 73

PRESENTATION AND EXPLANATION OF THE EMPIRICAL FINDINGS .................... 75

7.1 DISCUSSION OF THE WORKSHOP PROCESS................................................................................ 75 7.1.1 PRE-WORKSHOP ........................................................................................................................ 75 7.1.2 PRACTICAL IDENTIFICATION ..................................................................................................... 76 7.2 PRESENTATION AND ANALYSIS OF WORKSHOP RESULTS......................................................... 77 7.2.1 OVERVIEW OF THE PROCESS ILLUSTRATION ............................................................................. 77 7.2.2 ACTIVITY 1................................................................................................................................ 78 7.2.3 ACTIVITY 2................................................................................................................................ 78 7.2.4 ACTIVITY 3................................................................................................................................ 79 7.2.5 ACTIVITY 4................................................................................................................................ 80 7.2.6 ACTIVITY 5................................................................................................................................ 80 7.2.7 ACTIVITY 6................................................................................................................................ 81 7.3 THE RELATIONSHIP BETWEEN CORE COMPETENCE, PRODUCT- AND PROCESS INNOVATION 82 7.3.1 THE CORE COMPETENCE IN CONTEXT ....................................................................................... 82 7.3.2 SUSTAINABILITY OF THE CORE COMPETENCE ........................................................................... 84 8

A COGNITIVE FRAMEWORK FOR ANALYSIS .................................................................. 87

8.1 BOISOT ........................................................................................................................................ 88 8.1.1 DATA, INFORMATION, KNOWLEDGE ......................................................................................... 89 8.1.2 THE I-SPACE.............................................................................................................................. 90 8.1.3 THE SOCIAL LEARNING CYCLE ................................................................................................. 92 8.2 NIGHTINGALE ............................................................................................................................. 95 8.2.1 BUILDING BLOCK 1: KNOWLEDGE AS A COGNITIVE PROCESS................................................... 96 8.2.2 BUILDING BLOCK 2: SCIENCE AS PATTERN ............................................................................... 98 8.2.3 BUILDING BLOCK 3: TECHNOLOGY AS ARTIFICIAL FUNCTION ................................................ 100

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8.2.4 8.2.5 8.2.6 9

THE DIRECTION ARGUMENT .................................................................................................... 101 THE INNOVATION CYCLE ARGUMENT ..................................................................................... 101 THE THEORETICAL FRAMEWORK IN PERSPECTIVE .................................................................. 102

ANALYSIS OF THE EMPIRICAL FINDINGS...................................................................... 105

9.1 A COGNITIVE VIEW ON PROCESS INNOVATION ....................................................................... 105 9.1.1 ECONOMISING ON DATA PROCESSING – BETWEEN TWO EMPLOYEES ..................................... 106 9.1.2 THE DEVELOPMENT OF COMPLEMENTING TECHNOLOGIES ..................................................... 108 9.1.3 THE RELATION BETWEEN AMSA AND TEST DATA FROM FULL SCALE ................................... 108 9.1.4 THE SOCIAL LEARNING CURVE (SLC).................................................................................... 110 9.1.5 A NOTE ON ORGANISATIONAL KNOWLEDGE CREATION .......................................................... 113 9.1.6 APPLYING KNOWLEDGE IN PROCESS INNOVATION ................................................................. 116 9.1.7 DEVELOPING AMSA 1 – A CASE EXAMPLE ............................................................................. 117 9.1.8 THE COGNITIVE PROCESS IN SUCCESSFUL INNOVATION ......................................................... 118 9.1.9 THE COGNITIVE PROCESS IN CONCLUSION .............................................................................. 120 9.2 OPTIMISING THE STARTING CONDITION IN PROCESS INNOVATION ...................................... 123 9.2.1 THE INTERPRETATION OF THE SCREENING COMPETENCE ....................................................... 124 9.2.2 THE BARRIER TO ABSTRACTION .............................................................................................. 124 9.2.3 EXTRACTING PROXY DATA FOR PROCESS INNOVATION .......................................................... 126 9.2.4 THE ABSTRACT INNOVATION SPACE ....................................................................................... 128 10

DISCUSSION ............................................................................................................................ 131

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CONCLUSION ......................................................................................................................... 134

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LOOKING BEYOND THE SCOPE OF OUR THESIS ....................................................... 140

13

BIBLIOGRAPHY..................................................................................................................... 142

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APPENDIX................................................................................................................................ 145

14.1 14.2 14.3 14.4 14.5

LIST OF INTERVIEWEES .......................................................................................................... 145 INDIVIDUAL INTERVIEWEES................................................................................................... 145 WORKSHOP PARTICIPANTS .................................................................................................... 145 PROCESS ILLUSTRATION ONLY .............................................................................................. 145 TECHNOLOGY DESCRIPTION .................................................................................................. 146

MICROTIT........................................................................................................................................ 146 AMSA ................................................................................................................................................. 146 MINI WASH...................................................................................................................................... 146 FULL SCALE WASH....................................................................................................................... 146 AST ..................................................................................................................................................... 146

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FERMENTATION............................................................................................................................ 146 PURIFICATION ............................................................................................................................... 146 14.6

PROCESS ILLUSTRATION ........................................................................................................ 148

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PART I 1 I N TR O D U C TI O N Technology intensive companies face many challenges as a result of today’s ever increasing dynamic business environments. They must be on constant alert to maintain their market position. Markets require increasingly advanced products and lower prices, while the product life cycle decreases due to increased competition and technological capabilities. Consequently the high technology organisation is under tremendous pressure to continuously introduce pioneering products to maintain and increase market shares. Meeting these requirements, the technology intensive company must have faith in the R&D department’s ability to constantly develop competencies adding to the commercialisation of innovative products. In addition, the R&D department also has to deal with a growing need for shorter production cycles and a tendency to increasing development costs in innovation projects, resulting in a pressure for high success rates, because slow and unsuccessful innovation projects can result in dire economic consequences due to the initial high capital commitment.

Hence, what emerges is a picture emphasising the importance of building innovative competencies and knowledge in technology intensive companies. The winners of today, and certainly of tomorrow, are companies investing in building innovative competencies and seeking to develop their competitive advantage in their core technology. Since the situation we have outlined concern most technology intensive companies we find it interesting to look further into the competencies and the cognitive dimensions of a company bound to prosper in such an environment. Succeeding requires that knowledge and innovation, to a much higher degree than previously anticipated, become the central concern in business performance. However, the means by which such intelligence is achieved is as complex and difficult as its principal ends.

Today we live in a post-industrial society in which knowledge has increasingly come to be recognized as a primary source of wealth. During the turn of the century the neoclassical economic agenda was jeopardized as a result the knowledge-based jubilation. Nevertheless, today a realistic balance appears between the knowledge-based society and sound economical thinking. It is important to take into consideration that the knowledge-based view shakes the otherwise economic safe-ground in neoclassical thinking, founded on linearity and

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equilibrium, where labour and capital are the resources in production of physical goods. In a knowledge society this is no longer sufficient resources. Business economy is no longer about physical goods only, but to a higher degree about knowledge goods, information goods and services. The situation has somewhat changed the way organisations inscribe the world, and so focus drifts away from neoclassical economic efficiency and into cognitive efficiency and innovation, which today constitute the crucial success factor and determine the long term survival of the firm in the high technology business environment.

The knowledge society shifts the position of power; it is not enough to possess knowledge, companies must excel in knowledge management to create and exploit new knowledge in a constant and rapid innovation process. Knowledge is not a static asset; it is a dynamic process breeding new knowledge. A prerequisite to lift such task out of the current static equilibrium requires a deep understanding of how knowledge, possessed by the members of the organisation, is created and exploited.

1.1 Process Innovation at Novozymes One such organization managing to thrive in a demanding high growth environment is the Danish enzyme producer Novozymes. We have chosen Novozymes as our case company due to a number of reasons. As we will more thoroughly account for later in this thesis, the enzyme business is based on bio technology with increasing importance in a growing number of industries. The enzyme technology is applied in for example the food and feed industry, textile and detergent industry, and the forestry industry. Further the enzyme technology is gaining ground within a number of new industries and is proving an important growth engine for the Danish economy. Within the detergent industry Novozymes has just introduced a new stainzyme, which is distinct from other detergent enzymes on the market, as it drastically reduces energy consumption used to wash clothes. Detergent enzymes comprise Novozymes' largest business area. This is a competitive market in which new products are continuously required to maintain sales and market shares. Novozymes expects within five to ten years to be able to supply detergent enzymes for removing almost all types of dirt at low temperature. Yet, without proper sources of knowledge, which can be exploited by the competencies held by members of the organization, high capital investments could be lost in the long run.

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Today Novozymes stands out as the world-leading biotech company within enzyme production. Besides leaning on a strong national system of innovation and 30 years of research, it is still admirable how Novozymes manages to remain on edge of the industry competition. R&D in Novozymes is performed at a world class level. The detergent industry is by far the most advanced industry for enzymes, and here lies the excellence in innovation processes in Novozymes. We have chosen to focus on one specific department, the protease discovery department that develops enzymes for the detergent industry. The product development process is performed within a highly competent team of about 80 people. What we find interesting is that the protease department from 1999 to 2004 has managed to reduce the product development process from 10 to 5 month which means they have developed a huge advantage relative to competitors. This time reduction in product development, we believe, is a result of considerable competence advancement, and it is our wish to come to an understanding of how such competitive advantage is achieved.

Being the best at product development of enzymes can be ascribed to the ability to process innovate. Process innovation in the protease department gives rise to a number of new technologies that enhance the speed at which new products are developed, and as a consequence increases the speed at which new enzymes are formulated and commercialised. Hence, process innovation is the prerequisite for successful and continuous product development, and therefore is makes sense to investigate the maiden ground of how knowledge is created and applied in process innovation. Essentially, Novozymes was chosen because we believe knowledge and competencies play a significant role in the success of the company performance.

In summary, the reduction of the product development cycle from 10 to 5 month and the resulting competitive advantage that has been achieved over the past five years is the obvious and initial reason for our interest in the case study.

However, behind the optimised product development process, the latent source of competitive advantage is to be found. The process development from 1999 to 2004 has produced a number of groundbreaking technologies that have significantly improved the product development process. To our understanding this competence development plays a crucial role in the current competitive advantage within the production of enzymes, and hence, we have decided to

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concentrate our research on the process development from 1999 to 2004 in the protease discovery department.

1.2 Research Question From the introduction above it should be clear that we are facing a successful company operating in an innovative business environment. Instead of focussing on a deliberate problem in a particular case, our point of departure concentrates around the success within a given company. Just like learning from failures it is important and interesting to learn from successes. For that reason we have been motivated to investigate the success in the protease department. As justified above, the process development can be characterised as a competence development going on in the protease discovery department from 1999 to 2004, and with this in mind we have composed the following research questions:

1. What are the constituents of competences leading to successful process innovation in the protease discovery department? 2. Why have the protease discovery department hitherto succeeded in fostering knowledge leading to competence development? 3. How can the protease discovery department exploit current knowledge and competencies in future advanced process innovations?

1.3 Specification of the Research Question To give the reader a clear understanding of the task at hand we will now elaborate on the questions above and how they are going to be addressed. The question will be answered from both theoretical as well as practical point of views.

The thesis is divided into two overall objectives. The first objective is from a competencebased perspective to analyse and present the process innovation in the protease department running from 1999 to 2004, thereby answering question 1. The need to raise the first question is because the process innovation it self is highly complicated and consequently requires a thorough analysis and clear presentation to first of all make sense to us as researchers and equally to make sense to you as the reader. The second and main objective of this thesis is to

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analyse the process innovation in the protease discovery department from a cognitive perspective, thereby answering question 2 and 3.

To acknowledge the structure of our thesis it makes sense to first present our reasoning behind the structure in reverse order. First of all, since question 2 and 3 is addressed from a cognitive perspective it requires first, a detailed understanding of the elements constituting the process innovation and second, an abstract conception of the process innovation as a whole. Because of this, it makes sense to identify the constituents leading to successful competence development in the protease department. This, at the same time, detailed understanding and abstract conception of the process innovation, we will provide by visualising the entire process development in a framework named the process illustration (see appendix). The process illustration will answer question 1. However, logically a number of steps are necessary before it is even possible and valid to present the process illustration. Firstly, we need to develop a methodology and a framework to identify the constituents of competencies representing the process development. Secondly, it is necessary as the first task of our thesis to define what the constituents of a competence are.

We will now present and elaborate on our research questions in the chronological order, and explain how the questions are going to be addressed.

To answer research question 1: What are the constituents of competencies leading to successful process innovation in the protease discovery department, it is necessary to answer two sub-questions:

1A

What constituents of competencies should our working definition include?

1B

How should a methodology and framework to identify constituents of competencies be designed and applied?

Sub-question 1A and 1B will be answered from a theoretical perspective, whereas the main question 1 will be answered from a practical perspective by means of a workshop in the protease discovery department.

Question 1A will be answered in chapter 3.

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Since we regard the process innovation as a competence development it is necessary to first identify and define what are the constituents of a competence. Therefore we will conduct a working definition that covers the relevant elements of a competence.

Question 1B will be answered through chapter 4 and 5. The working definition will be used to develop a methodology and a framework to identify and present the constituents of the competence development from 1999 to 2004. This task is performed in a workshop in the protease department. In chapter 4 we will discuss different methodologies, proposed by a number of authors, developed to identify competencies or core competencies of organisations. However, since we wish to identify the constituents of a competence, and not the competence it self, the theoretical frameworks will be used as scientific validation and as inspiration to design and apply our framework during the workshop. One task is to design the framework to visualise the constituents of the competence development, another task is to develop the methodology used to apply the framework during our workshop in the protease department. Both tasks are discussed and presented in chapter 4 and 5, and with sub-question 1A and 1B in place we can now turn to answering the first main question.

Question 1 will be answered in chapter 7. The working definition, the methodology and the framework conducted through chapter 3, 4 and 5 provide the basis for holding the workshop in the protease department. The result from the workshop is a visualisation of the entire process development from 1999 to 2004 in form of the process illustration. A subsequent analysis and explanation of the process illustration, we will answer question 1: What are the constituents of competencies leading to successful process innovation in the protease discovery department.

Question 2 and 3 will be answered through chapter 8 and 9. In chapter 8 we present the cognitive theory that provides the basis for analysing the process illustration conducted during the workshop. In chapter 9 we will analyse the process innovation from a cognitive perspective based on the theoretical framework in chapter 8. While doing so, it is necessary to discuss the process innovation in the abstract, and thus move away from our current perception of the process illustration. This way we can deduct the underlying cognitive processes driving process innovation and explain why the protease

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discovery department hitherto has succeeded in fostering knowledge leading to competence development. Finally, based on our answer to question 2 and a subsequent reconsideration of how knowledge is created and exploited in process innovation, we will answer how the protease discovery department can exploit current knowledge and competencies in future advanced process innovations, thereby answering question 3.

In summary of our first objective, the working definition of competence, the framework for analysing the process innovation, and the methodology for conducting the workshop altogether provide the basis for conducting the workshop in the protease department. The result of the workshop is the process illustration explaining what constituents are leading to successful process innovation in the protease department. This concludes question 1. In summary of our second objective, the two main cognitive theories in coherence answer first question 2 and subsequently question 3. Below we present a model illustrating how the research questions are structured and answered through this thesis.

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Working definition

Methodology

Research Question 1

Workshop Process Illustration

Boisot I-space

Research Question 2 & 3

Nightingale Cognitive model

Analysis of the process innovation

Summary and Conclusion

Figure 1.1: The rationale for answering the research question.

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1.4 MOT perspective In the following we will briefly relate the theme of the thesis to our background as Management of Technology (MOT) students, and future MOT-practitioners. The academic and practical interrelation has greatly influenced our choice of theme and research question.

A central part of the MOT academia concerns understanding innovative challenges in technology intensive companies to promote competitive advantage. Involving several dimensions, MOT requires multidisciplinary and integrative skills and knowledge of both internal processes and business environment to identify issues that may either advance or impede innovative performance and hence competitive advantage.

Competence theory occupies a significant role in the strategic management literature and is as such a central element in the MOT perspective. Even though it is a well versed theoretical area, few if any scholars have focused on developing a framework to identify constituents of competencies as basis for analysing a process innovation. We believe that our focus will allow us to develop a terminology and practical framework that provides a deeper insight to the theoretical ambiguity in competencies to better understand the innovative dynamics in technology intensive companies. The ability to acquire a thorough understanding of a given process innovation, by identifying and mapping constituents of competencies is wider applicable, and in our opinion a valuable skill for future MOT-practitioners.

The empirical case is chosen with a MOT perspective in mind. As touched upon in the introduction, Novozymes operates in a high technology business environment with huge growth potential, and based on bio technology, the enzyme product takes up an important position in a growing number of industries. The potential application of Novozymes’ technology is considerable, and we find both the industry as well as the complex technology very appealing. Moreover, the chance to examine a successful company like Novozymes allow us to apply a great proportion of the literature examined within management of technology.

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The ability to comprehend such complex technology and the ability to develop and apply a theoretical apparatus necessary to analyse the process innovation may prove valuable in a number of subsequent job positions. Through this thesis we expect to enhance this ability and prepare ourselves for future MOT practising.

1.5 Purpose and target groups The purpose of this thesis is twofold. Having decided to base our thesis on a case study of the Danish enzyme producer Novozymes, part of the purpose obviously is to conduct a valuable and useful contribution to the company. We hope this research will provide the protease department with a different and possibly profound theoretical and practical perspective on the process innovation in question. The second purpose of this thesis is to demonstrate our ability to cope with a complex theoretical field within MOT, and to do so in relation to a high technology case. We would like to remind the reader, that above all, and despite our curiosity to question many different aspects related to the case, the academic requirements determine the final direction of this research. We believe however, that we have reached a fine balance between practical relevance and academic requirements.

1.6 Structure and overview Before moving on we will outline the structure of this thesis. We have chosen to structure the thesis in four main parts, I) Introduction, II) Development and application of methodology and framework, III) Cognitive framework and analysis, and IV) Conclusion.

Part I includes the first two chapters where we draw up the problem formulation and the methodological considerations. The introduction, which we have just discussed in chapter 1 sets out the scope of the thesis, followed by the methodological considerations in chapter 2, where we state how we will analyse the research questions.

Part II consists of several chapters. Chapter 3 starts out by giving a brief introduction to the field of competence theory to establish clarity due to considerable terminological confusion. We also introduce our focus on constituents of competencies in the competence theory. Chapter 4 more thoroughly explains what competencies consist of and hence what are the constituents of competencies. The chapter also introduces our working definition of competencies and presents an analysis of each constituent.

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In chapter 5 we begin the preliminary steps in developing the methodology and the framework to identify constituents to competencies in the protease discovery department. The chapter discusses four other methodologies to extract valuable elements to be used in the development of our own methodology and framework. In chapter 6 we develop the methodology and the framework based on the findings in the preceding chapter and we present the methodology and framework step by step. In chapter 7 we turn to the empirical case and introduce Novozymes and the protease discovery department. Further we present the screening processes in 1999 and 2004. In chapter 8 we apply the previously developed methodology and framework and present the empirical findings in the process illustration. Further we divide the process illustration in a number of actions and describe them in detail. Finally chapter 8 introduces a model to elucidate the relationship between product development, process innovation and core competence.

Part III includes three chapters and is the analysis of the empirical findings. By way of the introduction we set out the theoretical foundation in chapter 9. We must stress that this is by no means an attempt at descriptive writing, but the theories applied are rather complex and it is important to have the theories directly available, because the subsequent analysis makes detailed references to the theories. Chapter 10 presents the cognitive analysis. In the analysis we apply cognitive theoretical perspective to the case and structure the chapter according to the two research questions so we first answer question 1 followed by research question 2. Chapter 11 discusses the use of theory. We discuss how we have used the strengths of the competence-based and cognitive theories and the implication of their respective weaknesses and how the theories complement each other.

Part IV contains two chapters. Chapter 12 is the conclusion, which answers the two research questions and presents concluding remarks regarding the preconditions. In chapter 13 we look beyond the scope of the thesis.

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2 M ETH O D O LO G Y This thesis concentrates around two main objectives. The first objective is to achieve a clear understanding of the process innovation in the protease department, thereby presenting the constituents of the competence development by means of the process illustration. The second and most central objective is to analyse the process innovation to explain how knowledge has been created and exploited in competence development up until now and potentially in the future.

The thesis is divided into a practical half including a workshop and a theoretical half including a cognitive analysis. To give the reader a clear understanding of how we are going to address the research questions in each half of the thesis, this chapter goes into detail with the methodological considerations necessary to validate our practical and theoretical approach.

To accomplish the two objectives outlined above a number of theoretical perspectives will be applied. In the coming section we clarify the overall scientific perspective. The subsequent section goes into detail with the application of certain theoretical perspectives throughout the thesis. Finally, we explain and validate the case study as methodology and how the workshop has been carried out in practise.

2.1 Theoretical clarification We would like to emphasise that the thesis draws on a number of different schools of thought within the field of strategic management. These different schools of thought can be thought of as different ‘lenses’ through which the research question can be viewed. This multidisciplinary approach is a natural and important consequence of the research question addressed in this thesis because, first, we develop a theoretical foundation and a methodology to analyse and comprehend the case. This is necessary to achieve an understanding of the situation at hand. Second, we apply a different theoretical perspective to analyse the case.

The thesis initiates from an overall resource-based view of the firm. We will make use of the various concepts within the resource-based view and competence-based view to conduct the working definition. The competence-based view of the firm is seen as the central area in the first half of the thesis concerning the framework and methodology for identifying constituents

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of competencies in the process development, and in the practical execution of the workshop. In the second and final half of this thesis, the analysis of the process development will take on a knowledge-based view of the firm. More precisely, we adopt a ‘cognitive view’ on the processes going on in the creation and exploitation of knowledge in competence development.

We are aware that these schools of thought or theoretical perspectives to some extent overlap and are intertwined, as well as building on concepts and ideas from other schools of thought (e.g. organisational behaviour and strategic management). However, to avoid indefinite regression in the discussion of the theoretical foundations of the thesis, it has been decided to focus on the theoretical perspectives mentioned above. In the following paragraphs, these different theoretical perspectives will briefly be described and presented. It is believed that they represent different approaches to address the research questions. There will be a final evaluation of how these schools support and complement each other.

The competence-based view of the firm stresses that it is the internal strength of the company, expressed by its competencies, and not the markets served that is the primary source of a sustainable competitive advantage. These competencies should therefore be the starting point for the strategy formulation process. The competence-based view of the firm emphasises that it is the development and deployment of unique and idiosyncratic skills, technologies and resources that is the foundation for achieving the competitiveness, growth and survival of an organisation (Hamel & Prahalad, 1994). The competitive advantage is perceived to emerge from value retention through an imitation blockage. The choice of the competence-based view of the firm as one of the theoretical lenses in this thesis is a natural consequence of our desire to study competence development (the process innovation) in Novozymes.

The competence-based view of the firm has its origins in or is influenced by a number of other ‘schools of thought’ within the field of strategic management and economics, and some of these have an indirect influence on the work. One of the most influential schools of thought is the resource-based view of the firm (Penrose, 1959; Wernerfelt, 1984) that emphasises the rent-generating potential of the resource endowment of a firm. The competence-based view of the firm can (from our point of view) be thought of as a managerialistic interpretation and expansion of the resource-based view of the firm.

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Another school of thought has a close relation to and influence on the competence-based view of the firm. The knowledge-based view of the firm, or rather what we will refer to as, the cognitive view of the firm (Grant. 1996a; Spender & Grant, 1996; Nonake & Takeushi, 1995; Boisot, 1995; Nightingale 1998) emphasises that the central competitive strength for a company operating in the unstable and turbulent world of today is the knowledge of the company’s employees as well as the ability to develop, disseminate and exploit new knowledge. Thus, from a cognitive view competitive advantage arises from value creation rather than value retention.

Consequently, we see the two schools as complementary. Competence-based theory sheds light on the elements constituting a competence and emphasises that (above average rate of return) competitive advantage is achieved through an imitation blockage, i.e. value retention; however it does not explain the mechanisms creating new competencies. Complementary, the cognitive theory emphasises the ability to create new knowledge as a result of internal processes in the organisation, i.e. the cognitive view emphasises value creation as the path to competitive advantage. Conclusively, the competence-based theory is useful to examine the constituents of a competence, whereas the knowledge-based theory is more appropriate to examine the cognitive mechanisms creating and developing a given competence.

2.2 Theoretical application Prahalad & Hamel (1990), Grant (1991), Leonard-Barton (1992), Hamel (1994), Dosi et al. (2000), Drejer (2002) together provide the theoretical foundation for deducting a suitable working definition of competencies that will be applied to determine the constituents of a competence in the process development from 1999 to 2004. It is worth mentioning that the working definition is developed for the particular purpose of identifying the constituent of competencies in the process innovation. For this reason, the working definition must consist of tangible elements that can be visualised in the process illustration during the workshop. Since various definitions of competences exist, we will learn against a number of authors defining the competence in tangible terms. Primarily, we will draw on the work by Dosi et al. (2000) and Drejer (2003) because they establish tangible definitions based on a broad representation in the competence-based theory.

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The framework we develop to visualise the process innovation is based on a wide selection of methodologies and frameworks with each of their own focus and theoretical perspective. So far, we have been unable to source other methodologies and frameworks identifying and visualising the constituents of competencies. We have scanned the literature for frameworks identifying skills (Klein & Hiscocks, 1994), key activities (Lewis & Gregory, 1996), core competencies (Boisot & Griffith, 2000) and core technologies (Drejer, 2002) and more, however, to our understanding no such framework already exist. Based on the theoretical foundation, we construct a framework tailored for the particular purpose of identifying the constituents of competencies in the protease department from 1999 to 2004. Likewise, and based on, among others, the authors presented in this thesis, we compound a methodology for holding the workshop in practice.

The theoretical perspectives applied in the last half of the thesis can be found within the knowledge-based view (Boist, 1995, 1998; Boisot & Griffith, 2000; Nightingale, 1998). We prefer to characterise this theoretical perspective the ‘cognitive view’. The cognitive theory is applied to analyse the process development in the protease department from 1999 to 2004 by means of the process illustration. Applying the cognitive theory to the process innovation is in fact what enables us to properly analyse the competence development, and thereby answer the research questions. It is worth emphasising, that without the initial application and analysis from a competence-based view, it would have been impossible to reach a complete comprehension of the process innovation from a cognitive view. Without a fundamental conception of the entire process development it is fruitless to apply the cognitive view as we would lack the necessary insight to comprehend and analyse the competence development. This process of reaching a cognitive realisation has been a far from easy, but it is the objective to pass this comprehension on to the reader during the presentation of the case and the empirical findings in chapter 6 and 7.

We can divide the cognitive analysis into a value creating perspective (Boisot et al. 1995, 1998, 2000; Nonaka et al. 1994, 1995, 1996), and a value exploiting perspective (Nightingale, 1998). We apply the “Information Theory” by Boisot (1995, 1998) to explain how economising on data processing and an abstract understanding of the internal processes in the protease department lead to process innovation and competence development. We briefly incorporate Nonaka et al. (1994, 1995, 1996) to emphasise the importance of tacit and explicit

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knowledge in organisational knowledge creation. Finally, we apply the cognitive model of innovation (Nightingale, 1998) to explain how scientific knowledge is ‘indirectly’ translated into process innovation, whereby knowledge in the department is exploited and turned into competitive advantage.

The combination of the competence-based theory and the cognitive theory gives rise to a number of questions. As a basic consideration it is worth questioning the validity of first establishing the working definition on the competence-based theory and thereafter, to analyse the constituents of competencies from a cognitive perspective. Basically the cognitive theory does not define competence, and so to compensate for this gab, definitions from the knowledge-based view are taken into consideration in the final working definition. This way we cover the elements from both schools of thought and find a good balance in the working definition. This is further supported by a deeper definition of one of the constituents of competence in terms of three knowledge elements, thereby closing the gab between competence-based theory and the cognitive theory. The three elements of knowledge will be discussed in chapter 3.

One could question why the theoretical contributions by Boisot (1995, 1998) Nonaka et al. (1994,1995,1996) and Nightingale (1998) are applied to analyse the process innovation from 1999 to 2004, when in fact they do not explicitly deal with process innovation. First of all it is important to notice that we are characterising the process innovation as a competence development, and as mentioned above, knowledge and learning is key to competence development. Therefore it makes sense to apply the cognitive view of how knowledge is created and exploited to analyse the competence development. This argument will be much clearer once we present the theoretical perspectives, but it is important to remember, that the competence-based theory perceive competitive advantage to sustain from value retention through an imitation blockage, whereas the knowledge-based theory perceive competitive advantage to emerge and sustain through value creation.

Conclusively, by applying the two theoretical perspectives we wish to emphasise their complementarity and synergies for analytical purposes, with the objective to understand why the protease discovery department has succeeded in creating and exploiting knowledge leading to successful competence development.

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2.3 The case study This section serves to explain how valid data to answer our research questions is obtained. We first explain why the case study, as an overall methodology for this thesis, is employed. Next we elaborate on the approach to collecting data and conducting interviews. Finally, arguments for choosing a workshop will be presented, and the practical execution of the workshop and supporting interviews will be validated. 2.3.1 The case study as methodology This thesis is as a case study. Our motivation to base our thesis on an in dept case study lies in

our curiosity to analyse and explain a practical case. We find it rewarding and valuable to work out this thesis in cooperation with a company as it allows us to exchange empirical and academic perspectives on a practical case, through the interaction with employees. It gives us a feeling of reality and the desire to make a notable difference.

The case study methodology is characterised by its ability to study complex empirical phenomena, in which the data generation is multifaceted (Andersen, 1999:163; Maaløe, 1999:33; Yin, 1994:8-91). This applies well to our objective to identify constituents and understand why the protease discovery department has managed to successfully develop competencies. Obviously, the research questions are related specifically to the protease discovery department and hence we can characterise the methodology as a single case study (Andersen, Ib, 1999:165). 2.3.2 Qualitative vs. quantitative research Within the case-study framework the qualitative research has been chosen, because it provides

an in depth analysis of the case at hand. As already mentioned, this is central to reach a complete comprehension of the process innovation. Qualitative research is often criticised for being statistical unrepresentative due to a limited number of interviews (Andersen, 1999:40). We acknowledge this criticism, but question whether the quantitative method provide useful data to obtain the understanding necessary in this case. We firmly believe that there are severe limitations to using for example questionnaires to examine the research questions we seek to answer. The reason is the complex nature of the process innovation, which makes it difficult if not impossible to report in a questionnaire. We keep this issue in mind when choosing the number of interviews to carry out and interviewees to engage, to ensure validity of our empirical research results.

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2.3.3 Number of interviewees To obtain clarity of the competence development we have been communicating with eight

employees in total. We conducted six individual interviews divided on four employees. Central to the data collection and to the comprehension of the case we conducted a workshop of three hours with seven employees participating. The question is, whether eight employees cover a sufficient foundation to answer the research questions. We believe it does, because we employ the case methodology including an intense workshop and individual interviews before and after the workshop, where the empirical case is analysed in depth, and in accordance with Kvale (1997:110), we emphasise quality over quantity. Obviously, we must consider whether the number of interviewees is representative in connection to the final conclusions. However, we do not believe that our conclusions, or for that matter general understanding of the competence development, would be fundamentally different if we chose to interview a larger number of employees.

Of greater importance is the way interviewees are appointed. The eight participants in the workshop are key employees carefully selected based on their role in the process innovation and their position in the protease department. Generally the case company appoints interviewees because they can better appoint interviewees with the necessary background knowledge. Leaving the appointment of interviewees to Novozymes’ decision requires clear communication from our side, but we believe that by carefully explaining the purpose of each interview it is easier for Novozymes to assign interviewees with the necessary background knowledge. 2.3.4 Individual or workshop interviews In the empirical research we have chosen to use both individual interviews and a workshop.

There are pros and cons of using both, but the fundamental choice must rely on an assessment of the number of participants necessary to obtain the required data. The individual interview is well suited to provide information about a specific area and offers a possibility for in dept analysis. However, the interviewer must be aware of data validity and is recommended to verify data by conducting more than one individual interview. Contrary, the workshop stimulates interaction and discussion leading to more nuanced and thorough statements and discourages extreme and untrue statements. Further, in general participants experience the workshop as more interesting and enjoyable than individual interviews (Andersen, Ib:1999:207). In our empirical research, we use individual interviews to obtain information

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about specific areas of the process innovation, whereas we use the workshop to acquire wider and general understanding. Combining the two types of interviews, i.e. breadth and dept, we believe, offers the right mix and a true and fair view of the process innovation. 2.3.5 Practical execution In the previous four sections we have explained the central considerations to ensure validity in

the data collection. Here we explain how we conducted the interviews based on the considerations above. Our primary source of data was a workshop supported by individual interviews before and after the workshop. There are several reasons why we have chosen the workshop over individual interviews as the primary source of data.

The case study concerns a very complex process innovation running over a 5-year period involving many employees with diverse practical and intellectual backgrounds. Since our objective is to piece together the different elements to re-create the main elements and the linkages between them, the task must be to represent all aspects of the process innovation in an interview. Hence, we find it unrealistic that one single person possesses detailed knowledge of the entire process innovation and in addition to this is capable of articulating such knowledge in a structured and comprehendible way. Another option to overcome this problem is to conduct individual interviews with employees possessing diverse knowledge backgrounds to cover the entire process innovation. However, two problems arise form this approach. Firstly, some of the employees who have played a significant role in the process innovation are no longer with Novozymes. Secondly, due to the complexity of the case it would be difficult to gather pieces of information from various interviews in one big picture and expect to obtain and piece together a fair, true and detailed view of the process innovation by our selves. Thus, we find the workshop a far better alternative than individual interviews. The composition of employees with diverse knowledge background and roles in the process development ensures that we cover all aspects and together the participants piece together the elements and re-create the ‘complete picture’ and moreover cover the roles of the employees who have left Novozymes. Also we believe that by creating a dynamic environment during the workshop, we can foster synergetic interaction among participants and interviewers generating a more detailed representation of the process innovation. 2.3.5.1 Pre-workshop interviews Before we carried out the actual workshop we conducted 3 pre-workshop interviews.

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The first two interviews were conducted with Kirsten VÌver Jokumsen (KVJO), Head of ADU and Niels Henrik Sørensen (NHS), Creativity manager. The purpose of the interviews was to obtain a basic understanding of the product development processes in 1999 and 2004 prior to the workshop. NHS was our contact person and he appointed KVJO as the other interviewee because as head of a sub-department in protease discovery she possessed the knowledge of all processes in the product development in both 1999 and 2004. Both interviews were semi-structured to maintain structure, while simultaneously trying to avoid constraining the interviewee in presenting their message (Andersen, Ib, 1998:206 ). We took notes during the interview to be able to recall the content after the interview. The weakness of notes is that it can be difficult to simultaneously be an active listener and pose relevant questions. We overcame this problem by dividing the two roles between us, one primarily taking notes while the other primarily concentrated on posing relevant questions. In addition to this, during the second interview we had the opportunity to clarify unclear issues from the first interview. The purpose of the third interview was to validate the methodology and framework of the workshop. Prior to the interview NHS received a copy of figure (XXX det er den teoretiske process illustration) and during a semi-structured interview we discussed the methodology and framework. We chose to conduct the interview with NHS, because he posseses a good methodological understanding and would be able to give us valuable feedback on strengths and weaknesses of the methodology and framework. The result of the interview was a few adjustments to tailor the framework to process innovation in the protease department. 2.3.5.2 The workshop As previously mentioned, the purpose of the workshop was to piece together the different elements in the process innovation to re-create the main elements and the linkages between them. Achieving this required input from participants with diverse backgrounds representing all aspects of the process innovation and hence the first task was to select the participants. We did not select the participant ourselves but discussed during the first interview with KVJO and NHS who would be relevant to appoint. Based on our discussion and guideline, KVJO came up with a proposal to select 3 and 4 employees from two sub-departments within the protease discovery department. We agreed to appoint the 7 employees because they represented all aspects of the process innovation. We acknowledge the risk that by appointing participants Novozymes

can

sort

out

employees

with

e.g.

critical views

(Hammersley &

Atkinson:1995:134). However we regard this risk to be insignificant because each of the 24


Process innovation in Novozymes

appointed employees had a central role in the process innovation, which to our assessment, was the main consideration to take into account in the selection process.

To prepare the participants for the workshop and to avoid unnecessary confusion about the agenda, we e-mailed, prior to the workshop, the methodology and framework conducted specifically for the workshop to analyse the process innovation. This way we ensured that the participants already knew the agenda for the 3 hour workshop. Besides, participants were asked to prepare a number questions in advance to ensure the process innovation was in fresh memory. To encourage a dynamic workshop with fruitful interaction we used a 2 x 4 metres wall sheet to visualise the process development (please refer to appendix 16.4). The participants were encouraged to actively help out piecing the process innovation together, by physically placing the constituents of competencies on the process illustration. During the workshop we observed the dynamic interaction and discussion among participants and how the process illustration materialised. In cooperation with the participants we managed to recreate the key elements in the process innovation. The workshop resulted in a comprehensive process illustration visualising the constituents of competencies in the entire process innovation.

We found it essential to be able to verify our understanding of the process illustration after the workshop. Therefore we recorded the entire workshop on video. The recoding turned out to be helpful in the subsequent analysis process. Reviewing the video recording was very valuable to observe the interaction between the participants and how they used input from each other to re-create the main elements of the process innovation. Thus we believe the video recording had several advantages. We were able to review the order in which the different constituents were pieced together on the process illustration and thereby how the process illustration materialised. We could also review who placed the different constituents onto the process illustration and it was valuable to follow the subsequent argumentation for doing so. Therefore, the video recording subsequently enabled us to develop a more detailed and comprehensive understanding of the process development that would otherwise have been lost. 2.3.5.3 Validation of empirical data collection Based on several reviews of the video recording we re-arranged the process illustration after the workshop to make it clear and easy to understand (please refer to appendix 14.3). We 25


Process innovation in Novozymes

assess the process illustration gives a fair and true representation of the process innovation, however we finally validated the process illustration in an interview with SFFE, Head of MB department. We chose to interview SFFE because he played a central role during the workshop and because he possessed a significant methodological understanding of the entire process innovation. Furthermore, he expressed a methodological conception conducive to our objective. During the interview SFFE validated the final process illustration.

Based on both the theoretical methodology and the case study methodology, we now feel well prepared to answer the research questions in a valid and comprehensible manner.

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PART II 3 INTRODUCTION TO COMPETENCE THEORY In this chapter we perform a number of steps to be able to answer sub-question 1A. First we discuss key concepts within the field of competence theory. The purpose is to establish clarity due to considerable confusion concerning exact terminology Lewis & Gregory (1996), Bogaert, Martnes and Van Cauwenberg (1992), Granat (1991), Dosi, Nelson and Winter (2000). Second the chapter serves to discuss our focus constituents within the theoretical field of competencies. Third, based on different views of what constitute competencies, we synthesise a working definition of competencies, which will prevail throughout this thesis. Finally we elaborate on each element in the working definition.

3.1 Terminological Euphoria “The diverse sources of literature have meant that the field lacks cumulative theory building. There is a clear need for consistent terminology if any operationalization is to be attempted� (Lewis & Gregory, 1996:146) Entering the literature on resource-based theory of the firm is like stepping into a swamp of self indulging anarchists, all trying to throw their contribution on the pedestal. This we will try to avoid by leaning on the terminological clarification by Dosi, Nelson and Winter (2000).

Grant (1991) makes a distinction between resources and capabilities. He argues that resources include items of capital equipment, skills of individual employees, patens, brand names, finance, and so on. But let alone, he claims, few resources are productive. Productive activity requires the cooperation and coordination of teams of resources. A capability is the capacity of a team of resources to perform some task or activity. This view is supported by Drejer (2002) who posits that resources must be combined, or integrated, to provide competitive advantage. In this context, capabilities should be regarded as the aptitude to activate resources and make them valuable. Dosi, Nelson and Winter (2000) suggest a different relationship where they claim individual skills, which is a resource, are the building blocks of organizational routines and routines are the building blocks of capabilities.

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First of all, Dosi et al. (2000:4) think of capability as “…a fairly large-scale unit of analysis, one that has a recognizable purpose expressed in terms of the significant outcomes it is suppose to enable, and that is significantly shaped by conscious decision both in its development and deployment”. These features, they argue, distinguish ‘capability’ from ‘organisational routines’. Routines are units or chunks of organised activity with a repetitive character. Hence, “routines are the building blocks of capabilities – although not the only building blocks of capabilities”. Likewise, individual skills are the building blocks of organisational routines, and in their view; “… clarity would be served by reserving the term skills to the individual and routines to the organisational level” (Dosi et al., 2000:5). Further, and consistent with this proposal, skill of the organisation would simply be “… the collectivity of skills possessed by individuals in the organisation, regardless of whether the skills are modular, organisation specific, or not organisation-related at all.” (ibid)

Another concept flotilla is the use of the word competence. Selznick (1957) was the first to introduce what has been referred to as distinctive competence. However, the idea of distinctive competence seems to be at least as close a relative of the organisation’s mission statement, or perhaps its ‘strategic intent’, (Hamel & Prahalad, 1989) as of its capabilities (Dosi et al., 2000:5). Differences exists; when Selznick (1957), and subsequently other authors, refer to standardized building blocks, apparently it is the incorporation of those building blocks that account for the distinctive part, i.e. the coordination and integration mechanisms are value creating whereas the building blocks them selves are relatively standardized. Contrary, while Selznick is right to emphasize the importance of incorporation of building blocks in competencies, capabilities theorists consider the technical building blocks as quite distinctive in their own right. This we believe applies to both concepts. Griffiths & Boisot (2000:2002) pinpoint another difference between the two. “Capabilities tend to be used to describe more broadly-based business processes than competence. Capabilities refer to the ability of a firm to deploy resources, while competence is used to describe the resource themselves.

Examining the article by Prahalad & Hamel (1990) Dosi et al. (2000) argue, that by ignoring a couple of arguments in the definition of core competence, “…we can move closer to the capabilities concept.” We can then agree that competitive strength is derived in a small number of capabilities clusters where organisations can sustain leadership position over time.

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Similar, this comes close to the concept of dynamic capabilities advanced by Teece et al. (1997:516): “We define dynamic capabilities as the firm’s ability to integrate, build and reconfigure internal and external competences to address rapid changing environments.”

Dynamic capabilities are not simply built through R&D investments. On the contrary, “… coordination between R&D and other functions, and often with suppliers or alliance partners, is of the essence. Coordination is needed, among other things, for effective identification and linking of technological options and market opportunities, and for identifying the strength and weaknesses of existing resources relative to the requirements of a new product or process” (Dosi et al., 2000). Hence coordination is an important part of building dynamic capabilities.

Dosi et al. (2000:6) conclude that “… the concept of ‘core competence’ and ‘dynamic capabilities’ point in the same direction, being broadly concerned with the firms’ ability to carry off the balancing act between continuity and change in its capabilities, and to do so in a competitively effective fashion”.

Consequently, even though core competencies and the dynamic capabilities perspective build on two different theoretical backgrounds, and regardless of finding differences in the nature of their generative constituents, we agree clarity is attained by conceiving the formation of the two concepts as virtually equivalent, i.e. same-same but different!!!

As a truce of past terminological euphoria, from now onwards, when we refer to e.g. underlying competencies or competence generating capabilities it is in essence one of the same. We are referring to the constituents of a competence, i.e. the building blocks that constitute core competencies or dynamic capabilities, which we will identify and analyse later in section 3.4

3.2 Our focus Now that we have attempted to clarify the terminological euphoria we will more specifically discuss our focus within the theoretical field of competencies. We have chosen to focus on the elements that constitute competencies and not the competence itself. In the following section we will discuss why we focus on constituents and relate our focus to the research questions.

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Historically, competence theorising evolved from the paradox why some companies maintain above average rate of return. The competitive advantage is perceived to emerge from value retention through an imitation blockage. It is the general opinion of the resource-based view that competencies can be perceived the key to competitive advantage. In a strategic management perspective it is important to make appropriate investments in building core competencies to sustain competitive advantage. On one hand, several authors have expressed their dissatisfaction with the static equilibrium framework of much of the product-market strategy literature (Wernerfelt, 1984; Barney, 1986). On the other hand, increasingly persuasive case examples of successful organisations that appears to have developed strategy around their existing and future competencies rather than simply market and competitor analysis, suggest setting core competences as future strategic objectives. Examples such as Sony’s core competence in miniaturisation of products and Honda’s core competence in small engines and trains support this argument. In this sense, in a strategic management perspective competencies play an important role and we must decide how we apply the concept of competencies to our empirical case analysis.

Identifying core competencies is important and can be a helpful strategic tool to focus e.g. future investment. However, relatively few firms claim to have correctly identified and fully exploited their core competencies or key activities, and when a firm fails to correctly identify its core competencies; it misses attractive opportunities and chases poor ones Snyder and Ebeling (1992:26). Thus we can synthesise that on a general note, identification of core competencies is vital for the success of any company. However, identifying core competencies is not enough, because it does not explain what the competence is and what it consists of. We need to dig deeper to understand more about the competence, i.e. how and why it developed.

Therefore, the purpose of this thesis is not to identify core competence itself, but to analyse what the competence consists of. Hence our focus within the competence theory is on constituents of competencies and in the following sections we will thoroughly discuss what these constituents are. Understanding the constituents of competencies is a necessary and valuable element in our further search to answer the research questions. Only by understanding the constituents of competencies can we explain the successful competence development in the protease discovery department.

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3.3 The constituents of competencies Terms such as core competencies Prahalad & Hamel (1990), distinctive competencies Selznick (1949), capabilities Grant (1991), Leonard-Barton (1992), Stalk et al. (1992) and strategic assets Aaker (1989), are all used to describe the bundling of employee skills, organizational assets, processes and technologies enabling the company to provide a particular benefit to customers in a manner superior to their competitors. In this sense competences involve firm specific and idiosyncratic skills, resources and technologies possessed or controlled by the company. Obviously, differences between definitions exist and the constituents claimed to make up a competence varies, but the general conception of competences is somewhat agreed upon. Due to the vast amount of definitions it will be impossible and unnecessary to review them all. Therefore we focus on a few we believe cover the most relevant aspects of the literature and those relevant to our conception. We do not intent to add yet another definition of competencies, as many already exist. However, as we focus on developing a framework and methodology for identifying the constituents to competencies, a slightly modified definition is required to better capture the tangible elements of a competence, and thereby taking competence management to an operational level. But this is purely a practical definition for our framework and not a general redefinition of the concept of competence.

Breaking down the concept of competence into describing the constituents is obviously difficult. Not only because we do not wish to violate the common perception of the concept, but also because the subsequent framework must capture tangible elements and their generative nature of competencies at a level of abstraction that allows for at least some degree of codification. It does not make sense to once again describe the constituents in intangible idioms. The methodology and framework for identifying constituents to competencies we develop in chapter 5 must produce useful results by elucidating the competence development over time. In developing a valuable working definition of competencies start by examining the definition offered by Prahalad & Hamel.

The most common and widely recognised definition of competence is offered by Prahalad & Hamel as: “… the collective learning in the organisation, especially how to coordinate diverse production skills and integrate multiple streams of technologies…“ (Prahalad & Hamel, 1990:82). Based on Hamel (1994) we can summarize their working definition of core

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competencies in five points. First of all, core competence is an integration of skills and technology. He emphasize that core competence does not conceptualise in terms of a single human skill or an individual team. Rather, they are an aggregation of coordinated human skills and an integration of technologies. Second, Hamel (1994) states that core competence is a product of learning incorporating both tacit and explicit knowledge implying that they are not directly translated into value in the sense of accounting. Third, core competence delivers a fundamental customer benefit. Fourth, core competence is sustainable in that they are difficult to imitate. Lastly, to qualify they must enable access to new markets by their deployment into a range of the firm’s products and services.

The definition as such gives us only a vague idea of what a company’s competencies actually are – and are not. It is difficult to conceptualise as well as plan for developing competencies when codified in broad terms. We need a far more tangible conception to pinpoint what are the constituents of a competence. Nevertheless, the definition itself will not translate the constituents of competence into manageable conceptions; rather it is our methodology of identifying constituents of competencies and the subsequent codification of the building blocks and their linkages that capture the correlation between constituents.

Similar to Hamel (1994), Dosi et al. (2000) and Drejer (2002) Klein, Edge and Kass (1991) argue that corporate skills, i.e. core competencies, are not identical to individual skills, but rather, they are a systemic property which arises from human knowledge, from capital equipment and from organisational structure and culture. Leonard-Barton (1992) takes an analogous view in defining core capability as the knowledge set that distinguishes and provides competitive advantage. It is given four dimensions: employee knowledge and skills, technical subsystems, managerial systems, and firm’s values and norms. Whereas Griffith & Boisot (2000) argue that core competencies can be summarised as firm-specific, generic, sustainable skills and knowledge which are a product of learning. Hence they incorporate both tacit and explicit elements and offer some kind of value-based functionality to the customer.

Based on the different definitions, as tentative collateral, we posit the four main constituents of competence to be: human skills, technology, organisational structure, and culture. The problem with those four elements is that the influence of culture and organisational structure

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on competence is very difficult to identify as well as conceptualise into the process illustration.

The literature on organisational behaviour often emphasise the relation between performance and organisational culture (Hill, Charles, W.L. (1998), Irwin McGraw-Hill). We agree culture is an important element affecting competencies and the way firms organise around them Hill (1998) However, it is impossible to capture all aspects of tangible and intangible constituents, while at the same time making sure our framework becomes valuable and describing constituents in intangible idioms. Due to the practical perspective in this research it will not make sense to try to identify and visualise links between culture and competence, and hence we leave this element out of our working definition.

Some of the constituents of competencies are, as pointed out above, separate elements that are neither rare nor difficult to imitate. It is not the individual elements alone that make up the competence; it is just as well the incorporation, i.e. the way humans are coordinated, the way technology is integrated and the way learning is accumulated and shared in the organisation, that transform the constituents into unique, firm-specific, sustainable competences. Thus, the difference between core competencies and the constituents is that the constituent it self does not have to be sustainable, in that they are difficult to imitate, in order to qualify as key to competence development. For example, a generic technology could serve as an important element by the way it is integrated with other technologies or skills in the organisation. Similarly, the individual constituents of a competence do not have to deliver a fundamental customer benefit to qualify, because it is the system of skills and technologies incorporated in products or services that provides the benefit to customers. It is somehow the organisational structure that provides the condition for such coordination and integration of those elements. Consequently, to better conceptualise the element “organisational structure�, we wish to consider structure as the principles for how skills and technologies are coordinated and integrated.

Synthesising the discussion above, we can now answer sub-question 1A. The working definition of competencies should include the following three constituents: human skill, technology, and organisation.

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3.4 Elaborating on the Working Definition Now that we have synthesised a working definition of competencies we will in this section attempt to present a more practical and manageable conception of the constituents of competencies. We use this understanding later in the theses when we analyse the empirical findings in chapter 7.

We acknowledge other definitions of competence in the literature (Hamel, 1994; Dosi et al., 2000; Drejer, 2002; Klein, Edge and Kass, 1991; Leonard-Barton, 1992) as being more universal, however, for the purpose of our methodology and framework we have excused culture as a constituent of competence. As mentioned previously this exemption serves to make our analysis more tangible, thus allowing us, by means of a framework, to identify and visualise the relation between different constituents.

In summary, we suggest that a competence consists of tangible and intangible elements that serve as building blocks generating the competence. However, our objective is to turn those intangible elements into more manageable concepts. •

Human skills o Specific knowledge o Integration knowledge o Deployment knowledge

•

Technology o Generic vs. customised o Single vs. system

•

Organisation o Coordination of key personnel possessing different types of knowledge o Integration of key technologies and key employees possessing different knowledge o Learning

The subsequent elaboration on those elements, we hope, will give the reader a clear conception of what are constituents of competencies. At first, it may seem illogical to define the constituent in more tangible terms, nevertheless, it becomes clear when analysing the empirical findings.

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3.4.1 Human skill Having specified human skill as the basic constituent of competence, it is reasonable to

discuss the concept of knowledge. As we already know, knowledge is intangible fundamentals that are difficult capture and evaluate. Therefore, our objective is to distinguish between diverse types of knowledge to better capture underlying relationships of skills’ influence on competencies.

This section serves as an introduction to the discussion and analysis of skills in the empirical part later in this thesis. It concerns the decomposition of a skill into three types of knowledge and the development of ways to characterise this knowledge. In this thesis, knowledge is considered as the central building block of a skill. Knowledge might reside in and be reproduced in a single human, organisational processes or forms of collaboration in groups, procedures and organisational routines and heuristics.

It must be emphasized that we do not attempt to discuss and define knowledge in renewed detail, as this is not the objective of our thesis and the methodology ahead. Further the subject has already intrigued some of the greatest philosophers over the past three decades and more. We recognise the importance of these discussions, but suggest this debate is continued elsewhere; meanwhile we will build our conception on the work by Nielsen (1998: pp. 53)

The definition of knowledge needs to be expanded in order also to include knowledge pertaining to the team or organisation. If beliefs about causal phenomena are shared among a group of people in an organisation, these shared beliefs will constitute what can be termed organisational knowledge (von Krogh, Roos & Slocum, 1994). Thus knowledge may reside both in individuals, where it constitutes the basis of their skills and abilities, and in teams, where it forms the basis of the competencies. Thus, individual skill itself is insufficient to constitute a competence; rather it is the clusters of skills, i.e. the team of skills, sometimes in combination with technology, that generate competencies. 3.4.1.1 Specific, Integrative and Deployment Knowledge Nielsen (1998:53) proposes that three types of knowledge; Specific, Integrative, and Deployment knowledge constitute a competence. He suggests that knowledge underlies both technology and competence, but since we are focussing on the skill separate from technology, we agree in that technology is knowledge embedded whereas skill is knowledge in action

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(Cook & Brown, 1999). Thus, as opposed to Nielsen’s (1998) view on constituents of competence, we are digging deeper into the constituents of skill and consequently distinguishing between technology and skill. The reason being that knowledge in its tacit form is difficult to conceptualise whereas knowledge in action can be observed and somewhat codified, and this is essential to the possibility of identifying and visualising the constituents of competencies in the process illustration. Knowledge is something possessed, and thought of as something we use in action, but not understood to be action. Knowing on the other hand is understood to be part of action (both individual and group action), thus as part of concrete, dynamic human action. (Cook & Brown, 1999:387). This will be much easier to identify and visualize in our framework.

Knowledge, as Nielsen (1998) emphasises, is concerned with “justified true beliefs” (Nonaka & Takeushi, 1995; Pinch & Bijker, 1887) and is perceived to be closely related to the ability to act. The potential value of knowledge, he argues, is only realised through actions. Knowledge is therefore seen as underlying skills and personal capabilities of the employees and teams in the organisation.

Figure 5.1 shows that knowledge is the all embracing constituent of technology, skill and hence competence. Albeit knowledge is the fundamental building block of all three, technology can be perceived as structured and materialised knowledge while skill is perceived as knowledge in action.

Knowledge

Technology

Competence

Skill

Figure 2.1: The relation between knowledge, skill, technology and competence.

While Nielsen (1998:53) characterises the first type, specific knowledge, as “… specific areas or bodies of knowledge, such as a technology or scientific discipline”, we would characterise

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specific knowledge as knowledge possessed by an individual or team about a specific technology or scientific discipline. We agree that the areas of specific knowledge can be seen as a representation of different knowledge domains; however specific knowledge in isolation does not constitute a competence since it will typically embed and integrate several different technologies and bodies of knowledge. Conclusively, skill is first of all specific knowledge about technology and scientific discipline. This type of knowledge will typically be possessed by a specialist or team of specialists. Similar to our contention, Cockburn & Henderson (1994) identified component competence as the “… possession of skills or assets specific to particular local activities with the firm”.

The second type, integrative knowledge, Nielsen (1998:54) refers to as “… an expression of knowledge underlying the “organisation” of the individual areas of specific knowledge”. This type of knowledge integrates different areas or bodies of specific knowledge. This act requires knowledge about how the specific knowledge about technology and scientific discipline can be integrated to form a functioning system. Such specific knowledge becomes valuable only when combined in unique ways thereby generating new competencies. Kogut and Zander (1992) refer to this knowledge as “combinative” knowledge and it is postulated that elements of this knowledge often are tacit and firm specific and thus difficult to transfer and copy. Similarly to integrative knowledge, Cockburn & Henderson (1994:64) identified “architectural competence” as the ability to integrate knowledge domains and found a positive association with research productivity. The integrative knowledge of an organisation thus allows it to make use of its specific knowledge; to integrate them together in new and flexible ways and to develop new skills and technologies as they are required. We include in our definition both the “architectural knowledge” defined by Henderson & Clark (1990), the communication channels and problem-solving abilities that develop between individuals and teams within an organisation.

Another aspect of integrative knowledge, as touched upon by Cockburn & Henderson above, is concerned with the ability of the organisation to absorb knowledge from external sources. Most external knowledge is overlooked by the firm, but the ability of the company to recognize and integrate this valuable knowledge depends on its “absorptive capacity” (Cohen & Levithal, 1990) about how to integrate knowledge from external sources into the competencies of the company.

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Deployment knowledge represents the third type of knowledge and thus completes the set of skills. Like the first two types, let alone deployments knowledge is invaluable, however altogether in context they mobilize other skills and technologies and create economic and commercial value for the company. Nielsen (1998:54) argues that “… the third type of knowledge … focuses on how to exploit the preceding two types of knowledge in the creation of competitive advantage”. Thus a skill or team of skills containing primarily deployment knowledge is concerned with the ability to mobilise and apply the company’s stock of specific and integrative knowledge in a purposeful manner. This type of knowledge dominates first of all the marketing and strategy department of the firm and is focussed on how to create value for customers, however we believe deployment knowledge has both external and internal aspects. Internally, this type of knowledge deploys specific and integrative knowledge to optimise processes and direct the focus on value creating activities inside the firm, - perhaps in relation to external needs.

Conclusively, different skills are dominated by different types of knowledge and hence creating a unique network of collaboration in the firm. By dividing skill into three types of knowledge we will be able to identify the interrelation between different skill-domains and how those domains link up to different technologies. In order to conceptualise the coordination and integration of skills and technologies, i.e. the integration knowledge, those mechanisms must be visualised in a comprehensive framework. This we will take into consideration when building our framework in chapter 5.

The discussion above tells us that human skill possesses all three kinds of knowledge. The difference between skills depends on the composition of the three types of knowledge, whereas the significance of a skill simply depends on its influence on the competence, i.e. whether it is central or peripheral to developing the particular competence.

The existence of human skill is a necessary but insufficient condition for the existence of a competence. Hence, we will now discuss the second constituent technology.

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3.4.2 Technology As already discussed, competencies have both a human and a technological element. Even

though we have mentioned the interplay between these elements above, it may be of use to clearly distinguish between technological and human elements.

As argued by many authors, technology is a product of human skills (Iansiti, 1997; Griffiths & Boisot, 2000; Drejer, 2002). However not all technologies in a company are products of human skills residing within the company. We could divide technologies into generic and customised technologies. Generic technologies then being those which are publicly available and thus supposedly do not create any competitive advantage. Customised technologies are then tailored by the firm for a particular purpose, thereby potentially creating a competitive advantage. This is important in determining which technologies are playing the crucial role in generating competencies. Returning to the discussion of knowledge above, a generic technology could prove valuable depending on the way it is integrated and deployed in the organisation. E.g. if a generic technology is incorporated in a unique manner into a system of technologies and/or skills it suddenly is the integrative knowledge residing within the firm that partly determines the value of technology. For this reason, we find it useful to also distinguish between a single technology and a system of technologies.

Conclusively, we propose that it is human skill that creates and determines the significance of a technology and whether it is generic or customised.

The existence of technology is a necessary but insufficient condition for the existence of a competence. Thus we will now turn to the role of organisation. 3.4.3 Organisation Organisation refers to the formal organisational structure and management systems under

which human beings function. For instance, planning and control systems, reward and pay systems, communication channels, hierarchy of responsibilities and tasks, and other formal organisational manifestations that will greatly influence the human beings and their actions. Obviously, it is very difficult to identify the linkages between competence and the characteristics mentioned above. To apply to our framework it has to be more concrete and practicable.

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For this reason, by “organisation” we mean the ability to create a functioning system though integration and co-ordination of skills and technologies. Basically, organisation creates the foundation and opportunity for skills and technologies to be integrated and coordinated in the most fruitful manner. The organisation determines the composition of employees, e.g. machine operators, chemists, engineers and managers etc. Thus, by integrating and coordinating different domains of skill and technology the organisation sets the premises for learning. Individual and organisational learning is then the mechanism by which the individual or the organisation develops new knowledge, which is a prerequisite for the development of the competencies of an organisation. We recognise the distinction between individual and organisational learning (Weick & Westley, 1996; Levitt & March, 1996), however as the difference is of less relevance to our analysis and beyond the scope of our thesis, we will not go further into this distinction.

Within the literature on the competence-based approach to competition, there is agreement on the importance of organisational learning and thus the development of new organisational knowledge in connection with the development of competence. However, little is known about how learning precisely is carried out. It is typically emphasised that fostering and supporting the organisational learning processes are at the core of the managerial responsibilities in the company (Bogner, Thomas & McGee, 1996; Pisano, 1994; Prahalad & Hamel 1990). Our objective is to develop a framework to identify and visualise how the organisational premises influence and foster constituents of competencies. The way skills and technologies are integrated and coordinated as defined above is relatively concrete and possible to visualise; the problem lies with learning and its intangible characteristic. Prahalad & Hamel (1990:82) suggest that “… competencies are the collective learning in the organisation, especially how to coordinate diverse production skills and integrate multiple streams of technologies”. Central to the definition is the emphasis on the integration and coordination of skills and technologies as well as the emphasis on collective learning.

In support of our conception Dosi et al. (2000:7) consider combinative capabilities as “… the firm’s ability to handle change by transforming old capabilities into new ones”. Two points about the nature of this transformation are emphasized: i) that firms produce new capabilities by recombining existing capabilities and other knowledge, ii) that the ability of the firm to do this is affected by the organising principles guiding its operations. i.e. formal structure, social

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relations, knowledge bases of individuals and groups within the firm (Dosi et al., 2000:7). It is those organising principles we wish to illustrate in terms of coordination, integration and possibly learning mechanisms.

Conclusively, to bring the perception of organisation to a more tangible level, it first of all needs to be simplified to structure the identification process, and secondly it has to be conceptualised in terms of the way human skills and technologies are integrated and coordinated whereby learning is effectuated. This way we can think of learning as a result of the coordination of the different types of skills and as a result of the integration of technologies and/or human skills.

This chapter is a valuable foundation in our attempt to fully understand the competence development in the protease discovery department. We have developed a working definition of competencies helping us to identify constituents of competencies and subsequently analyse the findings. The working definition and specification of the constituents allow us to distinguish between different of skills and technologies, which in sum give us a deeper understanding of the process development in the protease discovery department. In the following two chapters we develop a methodology and a framework to identify and visualise constituents of competencies. However, only little has been written about the identification of constituents of competencies, as most literature focus on core competencies. Hence we will use this literature as a basis for developing a new framework for identifying constituents of competencies.

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4 DISCUSSION OF FRAMEWORKS TO IDENTIFY COMPETENCIES In the following two chapters the necessary foundation to answer sub-question 1B is established. First step is performed in this chapter where we present four different frameworks to identify competencies. We use the frameworks to extract valuable elements we can use as inspiration to develop a new methodology and framework to identify constituents in the following chapter.

When developing the methodology we need to take a number of elements into account that form a central part of the framework. First of all, the framework must be operational, and provide valuable insights regarding constituents of competencies and their linkages. Secondly, we can tailor the framework, because we know that the unit of analysis is the protease discovery department. Finally the framework must have a dynamic dimension since we seek to identify constituents over a five-year period. Hence we have a good conception of some of the characteristics the framework should have, but the elements do not specifically advice us exactly how to design the methodology. To develop a better understanding of how to design the methodology for it to match our specific requirements, we seek inspiration in other frameworks.

At first glance, one may expect that frameworks, with the characteristics just mentioned, already exist. However this is not the case. On the contrary, we have scanned the majority of frameworks to identify competencies, including Collins (1994), Tampoe (1994), Klein and Hiscocks (1994), Lewis and Gregory (1996), Hitt and Ireland (1995), Collins and Montgomery (1995), Drejer (2002), Iansity and Clark (1994), Leonard-Barton (1992), Griffith and Boisot (200), Miyasaki (1995) and Henderson Cockburn (1995), and unfortunately only a few offer valuable insights in relation to developing a framework to identify constituents of competencies. None of them offer a comprehensive methodology, which may be used in our identification process. Consequently we will present and analyse strengths and weaknesses of the four most valuable frameworks. This will be done in order to extract elements of the framework which are found useful, and which may be used as a source of inspiration when building our own framework in the subsequent chapter. The four methodologies, we focus on, differ fundamentally in their overall approach to competence identification. Some focus on competencies, while others simultaneously focus on competencies and constituents of

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competencies. For the purpose of inspiration when building our own framework, we do not find this distinction important, while it is not the focus itself, but the underlying methodological drivers used for identification that we find useful.

The framework analysis is divided into four sections. First we outline the framework. Next we present the methodology, and based on this we analyse strengths and weaknesses. Finally we summarize the elements we find useful in our subsequent development of a tailored framework to identify constituents of competencies.

4.1 Klein and Hiscocks (1994) Klein and Hiscocks (1994) outline various techniques to be used in competence analysis and strategy formulation. One technique is skill mapping which assists an organization in identifying key skills. A second technique is a skill cluster analysis, which allows an organization to see how skills are clustered “and therefore suggests the constellation of skills which would constitute core competencies”. Klein and Hiscocks (1994:185) define skills as more specific, and therefore more numerous, aspects of the firm – particularly their technologies, whereas a competence is a broad and aggregated resource including those skills. The identification of an organisation’s skill-base is itself a skilled activity. 4.1.1 The methodology - skill mapping The first step in skill mapping is identifying the individual skills highlighted in the

organization by means of interviews; those easily evident from products or services; and, those evident to customers or market watchers. The second step is benchmarking against competitors. The final step is determining which skills the organization considers most important, and thus a key to competitive advantage. The skills are ranked accordingly, and the highest ranked skills are labelled strategic skills. 4.1.2 Strengths and weaknesses of the methodology In accordance with our contention, Klein & Hiscocks (1994) argue, that the most useful skill

maps, are those who are tailored to the particular organisation rather than generic. This we consider a very important point when applying a methodology. However, a main benefit of Klein & Hiscocks’ methodology is its relatively simple application. Many frameworks are highly time-consuming and thus impracticable to operational management. In our opinion, the identification process in stage 1 should preferably aim at being carried out in one day.

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Certainly, there is a trade off between quality and cost of the exercise, but as we will see later, some frameworks are too complicated and scientific, and thus neglect the importance of management usability.

During the first stage of their methodology it is important to consider the level of aggregation, i.e. a too specific and disaggregated list of skills becomes uninformative, and similarly, if the list is too aggregated it becomes uninformative. This dilemma is one of the methodology’s major weaknesses. No optimal balance of aggregation is defined, and the issue may easily end up in a discussion between individual interests in the organisation. Nevertheless, the dilemma transpires in most frameworks due to the character of the skills identified, i.e. some skills are considered imperative whereas others are not depending on the role and interest of the individual interviewee. Consequently, the methodology calls for a delimitation of when skill is relevant.

The interviews, Klein & Hiscock explain, should be carried out with group leaders or department heads depending on the scope of analysis and the size of the organisation. Evidently, the scope of analysis, e.g. a functional, departmental or a SBU focus, determines which type of employee is interviewed; however, we believe the emphasis should be on finding the most appropriate key personnel rather than on finding interviewees according to their rank and title.

The second stage reveals valuable information about the capabilities relative to competitors. Most remarkable, not only do they suggest looking at overall skills, Klein & Hiscocks emphasize the importance of investigating the underlying factors independently. The factors evaluated are similar to Drejer’s (2002) definition of a competence, and to our contention of competence generating capabilities: human resources, capital equipment, organisational structure and culture. In practise, they explain, this is done by interviewing within the organisation, identifying capital equipment, culture and management control systems, evaluating intellectual property and comparing product features with competitors’ products, and by speaking with industry watchers (Klein & Hiscocks 1994:195). Even though we find this part of the methodology very relevant, they do not explain in detail how the results are conceptualised. The capabilities are ranked on a five-point scale from No capability to Worldleading capability, but as such, they give no examples and the results thus seem highly elusive

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to the reader. The categorisation is comparable to the consideration by Griffith & Boisot (2000) whether the competence (technology) is codified and diffused, which determine the relative value of the competence, however, it does not take into consideration the uniqueness of the capability.

During stage three of the methodology, the skills are evaluated on their importance to products or markets. If the skill proves important to either the product or to the market or preferably both, it is labelled a strategic capability. Let alone, the evaluation is important strategically, however as mentioned above, the authors lack a more thorough evaluation of whether the skill is unique to the organisation, or whether it is commonly possessed by industry players.

The skill mapping exercise is a precursor to other tools developed by Klein & Hiscocks (1994): The opportunity matrix, the skill base simulation, and the critical skill analysis. However they are of more strategic character, and mainly meant as applications for further analysis. The skill cluster analysis, on the other hand, is a valuable tool, which we will now investigate. 4.1.3 The methodology – skill clusters The skill cluster analysis indicates which products require which skills. Skills that are

associated with each other in the highest number of products are labelled core competencies. The skill cluster analysis builds on skill mapping and the opportunity matrix to allow possible core competencies to be identified scientifically by identifying the clusters of skills, which exist in a company’s operations (Klein & Hiscocks, 1994:204). 4.1.4 Strengths and weaknesses of the methodology The methodology was one of the first to identify core competencies scientifically (Klein &

Hiscocks, 1994:204). In comparison to methodologies of today, the tool seems poor, and lacking on particularly one theoretical dimension. Scientifically identified competences are easily misinterpreted and misleading. The fact that a skill is deployed in several different products does not necessarily qualify as a core competence. In terms of Prahalad & Hamel (1990) a competence is difficult to imitate, and Boisot (1995) claims they must be undiffused in order to represent a unique source of competitive advantage. The skill cluster analysis does not take these criteria into consideration

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directly, but rather indirectly it relies on the skill mapping selection process carried out at an earlier stage. On the positive side, the skill cluster analysis is effective in visualising the core engagements and interrelations of the company in a comprehensive and effective way. Further, when applied to possible future skills and products, it shows how competences may need to change. 4.1.5 Sum-up of valuable elements The analysis above highlighted a number of strengths and weaknesses of the framework. In

this section we summarize the elements we find valuable and a source of inspiration when developing our own methodology. First, we find the point about tailoring the framework to the particular organisation to be strong. Klein and Hiscocks (1994) also taught us about the value of a simplistic framework. Furthermore the framework drew our attention to the importance of the level of aggregation in skill identification, which is easily transferable to also incorporate other constituents. Finally skill cluster analysis reminds us how key linkages between skills offer valuable information about a firm’s core competencies. This is not directly applicable to our methodology, but we can use the idea of visualizing key linkages between skills.

4.2 Lewis and Gregory (1996) Lewis and Gregory (1996) present a practical approach to competence analysis. It begins by building a graphical model of firm activities (competencies) and resources providing a framework for evaluating and making explicit managerial perceptions of firm competences. The approach involves a series of internal and external interviews/questionnaires and consists of four phases. 4.2.1 The methodology The first phase is intended to identify the firm’s activities and constituent resources. Three

distinct elements are addressed 1) identification of generic top-level activities 2) decomposition and analysis of activities 3) analysis of resources associated with activities. Both activities and resources are visualised in a graphical model. The second phase analyzes the firm’s internal environment. Three different elements are examined 1) business planning processes 2) firm goals 3) company strategy for achieving those goals. Open-ended and business planning process questionnaires are used to collect the data needed to perform the analysis. The third phase derives core and distinctive competencies. Filtered data from the

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first two phases is used as input to a series of internal and external interviews enabling a provisional list of firm competencies to be generated. The list is compared with goals and strategies identified earlier, and core and distinctive competencies can be selected. The fourth phase is a review process, which involves a workshop session where all aspects of the project results, implications, conduct etc. are discussed. 4.2.2 Strengths and weaknesses of the methodology Lewis and Gregory’s (1996) attempt to provide a tool of practical value to managers is based

on a number of development criteria. The authors have, in their own words, tried to make the framework definable, reliable, viable, efficient in terms of resources used, visible, transparent and easily understood. According to Lewis and Gregory (1996), an important element in making the framework easily understood, is the use the power of pictures and diagrams, i.e. visualising aspects of the framework Tufte (1983, 1990). We support this view, and find the use of visualisation in phase one very illustrative. It provides a valuable overview of linkages between activities (competencies) and resources that may otherwise be difficult to convincingly describe. Hence we believe that Lewis and Gregory (1996) prove the point that complexity can be decreased by means of visualisation tools. However before such visualisation can be performed, we lack a definition of activities and resources, and how they are identified. A major weakness of the framework is that Lewis and Gregory (1996) refrain from defining in detail what the graphical model should illustrate, and how it should be identified.

Phase one also involves an analysis by participants of resource uniqueness by evaluating resources against a set of criteria. Such analysis may be valuable, but we question whether simple discussions of which firm resources are unique actually manage to address issues of strategic relevance, as we believe that such analysis should be performed thoroughly to add any valuable insight. On a more fundamental note we do not necessarily believe that resources have to be unique to be valuable, as suggested in the literature by Dierickx and Cool (1989), Grant (1991), and Amit and Schoemaker (1993), and supported by Lewis and Grgory (1996). As we argue in chapter 5 competence generating capabilities can individually be imitable, plentiful, codified etc and still offer, at an aggregate level, a unique competence as long as they are integrated in a unique way. Thus we find the use of criteria helpful in directing discussion of resources but not as a measure of resource value. Nevertheless, we find the use of criteria helpful to assess the value of competencies, because value is a direct function of 47


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imitability, Prahalad and Hamel (1993). However, generally speaking we assess the analysis of resources’ uniqueness to be a valuable process, because it makes participants question the resources they have identified.

As opposed to most other frameworks we have scanned, Lewis and Gregory (1996) analyse both activities and resources, equivalent to competencies and constituents in our terminology. We find the underlying methodology of decomposing competencies appealing because it increases the understanding of constituents of competencies. We are aware that this is not the primary objective of Lewis and Gregory’s (1996) framework, but it is a valuable derived result.

In the second phase the strategic aspect of the framework is revealed by analysing business planning processes, firm goals, and company strategy. The analysis is combined with the results from the identification in phase one to account for input to phase three where distinct and core competencies are derived. From a strategic competence perspective we assess the method to be valuable, because data from phase one and two is subject to internal and external analysis, and based on the analysis distinct and core competencies can be identified. Hence competence identification is based on a thorough analysis process involving initial identified competencies and constituents of competencies being evaluated against a set of criteria including uniqueness, goals, and strategy to validate that the correct distinct and core competencies are identified. Even though such strategic analysis is outside the scope of our thesis, we find the method solid and valuable. 4.2.3 Sum-up of valuable elements We have identified a few elements from Lewis and Gregory’s (1996) framework that will be

valuable when developing our methodology. First of all we believe the visualisation of activities, resources, and their linkages constitute a very valuable source of inspiration. However we do not find the visualisation methodology directly applicable to our methodology, but the fundamental principles and advantages of visualisation in general are definitely transferable. Secondly Lewis and Gregory (1996) have demonstrated how a framework with a dual focus can identify both competencies and constituents of competencies.

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4.3 Drejer (2002) Drejer (2002) outlines a number of issues in competence identification and analysis. The issues are not presented in a structured manner as for instance the frameworks by Klein and Hiscocks (1994), and Lewis and Gregory (1996), but is according to Drejer (2002) an attempt to free the decision making process from its more generic content side. To maintain overview we present Drejer’s (2002) 9 issues in competence identification and analysis as bullet points below. 4.3.1 The methodology 1. Establish a common understanding of what constitutes competence and core

competence, and what competence means for the individual firm. 2. Select the appropriate unit or level of analysis, i.e. corporate, divisional or functional level. 3. Identify a full list of competencies covering the entire firm without consideration to strategic importance. 4. Select the most important competencies from the list. The list is reduced by approximately factor 4 5. Map the competencies to provide the foundation for competence analysis. 6. Select a few core and focus competencies from the remaining list. The selection is based on thorough strategic analysis of importance to customers, markets and other stakeholders. 7. Formulate a portfolio of the chosen core and focus competencies to get a first impression of the competence development 8. Validate that the identification of the selected competencies is not a result of habit, power, or culture. 9. Decide who should be part of the identification and analysis team i.e. how top management and other members of the organisation.

4.3.2 Benefits and weaknesses of the methodology Drejer’s methodology embraces wider than other more structured frameworks by covering

several aspects simultaneously. Since the methodology is entirely focussed on the identification of competencies as a necessary foundation for competence development, there

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are aspects of the methodology we will refrain to comment on. Hence this section will focus on a few of the issues presented above.

As the framework by Klein and Hiscocks (1994), Drejer (2002) advocates that the most valuable methods for identifying competencies and analysis are tailored to the individual organisation. The issues Drejer (2002) put forward should be considered as guidelines and considerations, and included in a tailored form. Another valuable aspect of Drejer’s (2002) methodology is to obtain a common understanding of how to define competencies in general, and the firm competencies in particular. We believe that this is very important, and argue that the ability to create a common understanding among participants can be interpreted even wider so as to encompass all key elements in any given methodology. Misunderstandings may be eliminated if all participants work from a common foundation and understanding. In the second issue Drejer (2002) focuses on the appropriate unit or level of analysis. In accordance with our contention, Drejer (2002) finds it important to determine at which organisational level the competence identification and analysis should be performed. We believe it is a question of the level of aggregation, as discussed in Klein and Hiscocks (1994), because a corporate core competence may also consist of a number of competencies at SBU level. Drejer (2002) does not attempt to provide clarity, but merely to draw our attention to the issue. Issue three to seven concerns the identification of competencies and the selection of a few core- and focus competencies. This process adds little value to our methodology, and will not be further commented. However, issue five – mapping of competencies – may add valuable insight. The purpose of competence mapping is to describe competencies in more detail, i.e. to get a deeper understanding of their linkages and characteristics. Drejer (2002) supports Lewis and Gregory’s (1996) view of the value of visualisation by suggestion a few models, which illustrate, and describe linkages and characteristics between competencies in more detail.

The last issue in Drejer’s (2002) methodology concerns who should be involved in the competence identification and analysis, and at which stages. We find this issue to be of uttermost importance and thus three main approaches to an organisational process: top-down, bottom-up, and a dual approach are now discussed.

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In the top-down approach competence identification is performed by top-management. The rest of the organization is not involved in the analysis process until much later on. Obviously the top-down approach must be utilized in consistence with the organisational level of the analysis, i.e. top-down approach is appropriate to identify competencies at the corporate level. The bottom-up process is radically different from the top-down process. The bottom-up approach examines the employee’s knowledge and perception of the competences before any analysis of what the top managers think. As with the top-down approach, it is important that the approach is applied according to the level of analysis. Hence Bottom-up approach is appropriate to analyse competencies at the divisional, functional or lower organisational levels. Finally, Drejer (2002) presents the dual approach. Evidently, top-down and bottom-up approaches each possess both advantages and disadvantages. Where a bottom-up process has an immense potential for getting access to employees’ valuable knowledge on competencies and constituents, it also runs the risk of getting lost in detail and/or departmental rivalry. The top-down approach contains the opposite risks. Considering the advantages/disadvantages of each approach, it seems reasonable to explore a combination of the two approaches in a dual approach, which “... may identify gaps between the top-level perception of competency and the actual reality of available capability...“ (Lewis & Gregory, 1996:41). The appropriate choice of approach depends on a number of issues such as company size, organisational level, and focus on competencies or constituents, but ultimately it is a question of weighing advantages versus disadvantages. 4.3.3 Sum-up of valuable elements Drejer’s (2002) methodology raises a number of important issues that become useful in the

subsequent development of our methodology. By merely suggesting a number of issues for consideration in methodology building, Drejer (2002) emphasises the importance of customization and tailored frameworks. Another valuable aspect is the creation of a common understanding in the identification process, and to be aware of the organisational level of analysis. In a similar way, Drejer (2002) draws our attention to involving the appropriate employees in the identification process.

4.4 Griffith and Boisot (2000) Griffiths & Boisot (2000) have presented significant work in the field of competence identification and analysis, based on a cognitive view of competencies. They identify 51


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competences at the aggregate level of performance like Klein & Hiscocks (1994), but from a cognitive perspective by implementing the C-Space where competencies are discussed in terms of knowledge. The authors present a model and a methodology developed to assist companies in the identification and discussions of core competencies, and to recognise significant tacit knowledge to avoid making it vulnerable for diffusion to competitors. The starting point for Boisot (1995) is that competence inimitability and value are two different things.

The methodology is based on Boisot’s (1995) C-space, where he argues that inimitability is related to low level of codification and low diffusion of knowledge, i.e. only a few people work with the competence in a very tacit manner. However, to be valuable, competence knowledge needs to be disseminated and used by many individuals in the organisation. Thus the knowledge must be codified for members of the organisation to gain access to it. Higher levels of codification unfortunately lead to diffusion of competence knowledge, and consequently increase the risk of imitation. Hence a trade-off between codification and value exists on one side, and diffusion and inimitability on the other side. The relationship is depicted in the figure below.

Codified

Uncodified Undiffused

Diffused

Figur 2-1: C-space Boisot (1995)

4.4.1 The methodology Griffith and Boisot’s (2000) methodology is workshop-based and involves 5 stages. The

objectives of the workshops are twofold: firstly, to identify and map technologies and their linkages, and secondly to facilitate a discussion about the meaning of the competencies and knowledge resources.

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Stage one concerns the pre-workshop preparation to assure that there is common ground before the actual analysis begins. Core competencies tend to relate to aggregated technologies, therefore the level of technology aggregation must be determined. Likewise a break-down of constituent elements is necessary. Furthermore it must be made evident whether the area of focus, the orientation, is marketing or R&D. Finally no less than 12 participants are appointed.

The second stage is introduction to the C-space and groupware-based facilitation if such software is to be used.

In stage three the analysis starts by identifying linkages between the elements of the technology. Two steps are performed. 1) Consensus must be reached on the technology elements, which were identified in stage one and 2) the linkages between these elements need to be identified and agreed upon.

Stage four concerns the actual mapping of technologies and linkages identified in the previous stage. Each participant individually inserts the technologies and linkages onto the C-space Boisot (1995) by placing each of them at a point on the codification and diffusion (industry level) scales. Next step is to review the individual results, and allow participants to discuss differences in order to agree upon a pattern.

The fifth and final stage is the interpretation of the C-space map. This is the core activity where participants can discuss what the data might signify in terms of core competence identification. 4.4.2 Benefits and weaknesses of the methodology We assess Griffith and Boisot’s (2000) framework as a valuable tool for strategic

management. Other competence identification tools tend to include too many aspects and elements of the competence theory, which may confuse the clarity the framework originally intended to provide. Griffith and Boisot’s (2000) methodology is highly focussed, and limits its scope to include the knowledge aspect of competencies. In short, a core competence is an aggregate level of a technology preferably considered to constitute a process or a system. Therefore, the core competencies identified by the company are selected on the basis of clear 53


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and comprehensive criteria, making the result much more transparent. For example, the technology to make a fibre might consist of: Pulp, mix, dissolve, filter, wash etc.

As a basis for strategic decision-making, a high level of transparency and abstraction is valued by management. However, from an operational management perspective, the results are ineffective. It focuses only on core competence identification, and consequently neglects to analyse and highlight any of the constituents of competencies that constitute the building blocks of the core competencies in the first place. One might think that the methodology offers a weak conclusion identifying core competences only on the basis of the knowledge aspect. It could very well be accused of being narrow-minded and thus not particularly valuable as it lacks the wider and deeper perspective. Nonetheless, we acknowledge the existence a trade-off between strategic and operational focus, which provides the breeding ground for our criticism.

Another interesting aspect to the methodology is the ease of use, as the methodology is developed with ease of use in mind. It is simple and very manageable for the participants. The drawback of the methodology is that it requires execution by consultants. It is impossible to execute for a company alone, because it requires a lot of underlying knowledge of the C-space Boisot (1996) to be a valuable exercise. Griffith and Boisot (2000) suggest no less than 12 participants to ensure full benefit of the exercise. The optimal number of participants depends on the situation but in general it is noteworthy that too many participants can make the discussion blurry and might make it increasingly difficult to reach a consensus.

The final point of criticism relates to the mapping of competencies. Participants map the degree to which the technology is diffused in the industry, this imposes a problem of objectivity since such an evaluation is indeed very subjective and most likely based on personal beliefs. Consequently we believe that it might be hazardous to the entire process partially to base core competence identification on qualified guesswork. 4.4.3 Sum-up of valuable elements We can use a few elements from Griffith and Boisot’s (2000) framework in the development

of our own methodology. We find the overall workshop approach to competence identification very appealing even though Griffith and Boisot’s (2000) workshop methodology needs to be tailored to apply to the characteristics we develop in the next 54


Process innovation in Novozymes

chapter. Another valuable aspect of the framework is the pre-workshop preparation, where a common ground is established before the actual identification is commenced. Also the preworkshop preparation appoints the participants.

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5 D EV EL O P I N G A M ETH O D O LO G Y A N D F R A M EW O R K By extracting valuable elements from four frameworks, the previous chapter provided the basis for answering sub-question 1B. In this section, the elements are used as inspiration for developing the new methodology and framework to elucidate the process innovation. Together, chapters 4 and 5 enable us to answer sub-question 1B.

The chapter is structured in five sections. In the first three sections the methodology and framework are developed. First the focus is on how to design the framework to best present the empirical findings. Second we concentrate on how to apply the methodology to best obtain the empirical findings. Third, a number of factors that need to be taken into considerations, when developing the methodology and framework are examined. The forth section presents the five phases methodology in a workshop and finally the theoretical framework is presented in a illustration in figure (XX)

5.1 The process illustration During the analyses in the preceding chapter we sought for a way to display the findings of the identification of constituents, in a clear and structured way while simultaneously capturing the necessary details. From the analyses we learned that the findings can be presented in a number of ways, and found Lewis and Gregory’s (1996) visualization of relationships between core competencies, competencies and constituents very appealing. However, as we argued during the analysis of the framework, the visualisation approach they suggest, is not directly transferable to our methodology. Hence to increase the value of presenting our findings in a visualisation, we need a more minute description and analysis of linkages between technologies, skills, and integration- and coordination mechanisms. Furthermore, we need to abandon Lewis Gregory’s (1996) static view, in favour of a dynamic view, capturing the central aspects of the 5-year competence development, i.e. we analyse the development process over time. We achieve this by describing the product development process in the beginning and the end of the competence development, showing the starting and ending conditions of the development process. Since the analysis stretches over a 5-year period, we suggest dividing the process illustration of the development process into a few natural phases to ensure structure, and secure that the entirety is not lost in a myriad of technologies, skills, integration- and coordination mechanisms. The stages also serve as milestones or snapshots of the competence development.

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Below we present a model of the process illustration, which may seem slightly complex at first sight. However, when applied to the empirical case it clearly illustrates important linkages between technologies, skills, coordination- and integration mechanisms. Hence, the strength of the process illustration is its ability to offer overview and structure of a complex process development, which we believe is unattainable if presented by other means, for example in writing.

57


1

A

1999

Phase I

B

3

2

We know the starting condition

4

C

D

Process innovation in Novozymes

Why did We learn

Stage 1: Competence improvement X

What did we learn

5

4

C

E

Phase II

D

F

6

G

7

Why did We learn

Stage 2: Competence improvement Y

What did we learn

C

4

Phase III

7

F

G

2004

We know the current situation!

58

8


Process innovation in Novozymes

5.2 The workshop Having established that we will present the findings in a process illustration, we will now focus on the identification process, i.e. which operational approach should we apply to identify the elements in the process illustration. The identification process can be performed in various ways. Klein and Hiscocks (1994) suggest structured interviews, and in a similar vein

Lewis

and

Gregory

(1996)

suggest

a

series

of

internal

and

external

interviews/questionnaires. Drejer (2002) does not specify how to obtain the information, whereas Griffith and Boisot (2000) suggest a workshop as the best process. There are obviously both strengths and weaknesses to the different approaches to identification, but based on our purpose and presentation method, we consider a workshop approach most valuable due to several reasons. As the reader have probably noted from figure (xx illustration of visualisation) the process illustration will be a slightly complex illustration of constituents and the linkages between them, implicitly implying that the identification of the constituents may be a complex process as well. We believe that a workshop constitutes a superior identification process, because it offers a dynamic and synergistic environment, in which participants can use input from each other to advance a detailed identification of constituents. Ideally participants with diverse knowledge backgrounds together cover the entire development process, and facilitate to bring forward the knowledge that would have been neglected in individual interviews and questionnaires. Our intention is that the participants, during the workshop, work together to draw up a more nuanced illustration of the competence development process than would have been the case had they been interviewed individually. During the workshop the visualisation-outline offers a strong tool of guidance, and helps to avoid the workshop from loosing direction or getting lost in detail, which is a common weakness of workshops. To make sure that the participants have similar starting point in the workshop we introduce a phase to create what Drejer (2002) refers to as “common understanding�. In this phase we assure that there is a common perception of what the core competence of the department is, and that the participants understand the terminology used during the workshop. The pre-workshop preparation is also used to ensure that technologies and skills are identified at the right level of aggregation. A given technology can be analysed individually as a system of smaller technologies, or broken up into smaller technologies that are analysed individually. These two scenarios obviously result in two entirely different visualizations, and hence it is essential that the identification is consistent. The workshop consists of five phases and the identification, and selection and prioritisation process follows 59


Process innovation in Novozymes

the pre-workshop preparation. The final phase is a review process in which the participants authenticate that the visualisation of constituents corresponds with initial identified core competence.

5.3 Other considerations In addition to the visualisation and the workshop, the analyses of the different frameworks offered a number of issues that we found useful to include in our framework. Klein and Hiscocks (1994) and Drejer (2002) stress the importance of tailoring a particular situation, and suggest that flexible and dynamic frameworks increase usability. We include this suggestion in phase one of the workshop. In the pre-workshop preparation we tailor the framework to accommodate the characteristics of the process development that is subject for the analysis. We build in flexible elements in the framework that can be adjusted to accommodate different kinds of development processes. As mentioned by Klein and Hiscocks (1994), another issue of concern is to make the framework simplistic in its use, and reduce time used on the workshop to a minimum. Obviously we develop the framework with ease of use in mind, and minimize the time used in the workshop. As the reader will note from the next section, where we present the workshop’s five phases in their entirety, the workshop, excluding the pre-workshop preparation phase, can be carried out in approximately 3-4 hours. The final issue we will present concerns the organisational process of the identification, and hence which level of the organisation should be involved and in which order. Drejer’s (2002) suggests three different approaches, but neither are directly applicable to our framework. Based on Drejer’s suggestion, we tailor his approach to match our needs. As previously stressed, we operate on the departmental level, and therefore we need to involve employees from a lower level who possess the necessary knowledge. However, during the pre-workshop preparation phase managers are engaged to discuss the core competence of the department, and during the actual identification process it is necessary to engage both mangers and employees from a lower level, who have knowledge of the process development. The number of participants depends on the complexity and the organisation being analysed, but we estimate to engage 1-3 persons in the pre-workshop preparation phase and 6-12 persons in the identification process.

5.4 The Methodology and framework In the previous sections we discussed the methodology and framework to identify constituents of competencies. However, to answer sub-question 1B this section serves to describe how to

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apply the methodology and design the framework. The methodology consists of five phases and the presentation of the methodology constitutes the answer to the part of sub-question 1B. The other part of the question will be answered through the presentation of the design of the framework. The design of the framework is presented after the presentation of the five phases of the methodology. Together, the five phases of the methodology and the presentation of the design of the framework answers sub-question 1B. 5.4.1 Phase 1: Pre-workshop preparation The internal taskforce or group of consultants who run the workshop need to prepare the

following prior to the workshop: •

A detailed description of the screening process before the process innovation initiates in 1999, (please refer to section 6.3.1)

A detailed description of the current situation of the innovation process in 2004 (please refer to section 6.3.2)

Suggestion to the department’s core competence

Proposal of 2-3 intermediate stages in the innovation process

Selection of the key personal to participate in the workshop.

The objective of the pre-workshop preparation is primarily to tailor the methodology to the company or department of analysis. Secondarily, it serves to save time (and cost) during the practical part of the workshop, and finally to prepare the selected personnel for participation. One or several managers carry out the pre-workshop preparation, i.e. an internal taskforce of the department or company and/or in cooperation with consultants dedicated to the research. It is important that the taskforce possesses the overview necessary to cover the entire process. To establish the best possible foundation for an effective workshop, several steps must be prepared. First, a thorough description and analysis of the starting point (e.g. year 1999) is performed to determine and characterize the situation, at the beginning of the development process. The second step is to perform a similar analysis at the end of the development process, i.e. the current situation. The difference between the two stages constitutes what we phrase “the competence development over time”. Step three involves coming up with a suggestion for the department’s core competence, but without making any presumptions that may harm the dynamic identification process. The suggestion later serves as a guideline for the workshop discussion. Step four is an attempt to identify the intermediate stages of the

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development process. The goal is to break down the development process into a number of intermediate phases, thereby making the discussion more structured and manageable when the actual identification of constituents of competencies is performed during the workshop. One could imagine that certain milestones or similar clear identifiable stages could be used for such purpose. The fifth and final step is to identify key employees who are essential to the development process and should participate in the subsequent workshop. 5.4.2 Phase 2: Common understanding At the opening of the workshop the first objective is to develop a common understanding of

the task at hand, and a common understanding of the different concepts employed in the methodology. The following issues are performed: •

Workshop discussion and validation of the core competence and the intermediate stages in the process innovation

•

Achieving a common understanding of what the constituents of a competence are: a. What is technology? i. Generic technology vs. customised technology ii. Single technology vs. system of technologies b. What is a skill? i. Individual skills vs. group skills c. Coordination vs. integration

To avoid misunderstandings during phase three, (the identification process), it is essential to reach a common understanding among the workshop participants on a number of issues. One thing is to suggest what is the core competence of the department and what are the intermediate stages of the development process, another thing is to obtain consent among the participants. This aspect is absolutely critical as a guideline for the discussion and thus for the success of the workshop.

The second phase consists of two steps. The first step involves a discussion and validation of the core competence, and the intermediate phases suggested in the previous phase. During this step the participants have the opportunity to come up with suggestions of the department’s core competence. Perhaps they have a different view on how the development process can better be broken down into intermediate phases. Such discussion must crystallize in a common understanding, which may be by all participants. 62


Process innovation in Novozymes

In the second step, a common understanding is sought among the participants’ conception of what constitute a competence. Participants are expected to follow and construe the definition of competence and the constituents of competencies presented. Firstly, technology is an important constituent of the definition of competence. There is a distinction between generic technologies that are commonly used in the industry and easily accessed, and customised technologies that are customised within the organisation for a special purpose. Furthermore we distinguish whether technologies are utilized individually or as part of a system. Another element in the definition of the core competence is skills. It is essential to possess a common understanding of the difference between individual and group skills, including when each skill is used in the identification process.

Finally, participants must know the distinction between coordination and integration. Coordination takes place between skills in terms of transfer of knowledge and information. Integration takes place between skills and technologies and in between technologies or skills individually. The integration of two elements will sometimes transform into a new skill or a new technology. Phase two is estimated to be performed in less than 30 minutes, preferably less. 5.4.3 Phase 3: Identification The identification consists of three steps and is estimated to last between 1-2 hours.

Step one involves a brainstorming and positioning of skills and technologies. The participants suggest and name different skills that have contributed to the competence improvement X on the map. Step two includes connecting the skills and technologies with coordination and integration linkages. It is important to identify which skills and technologies lead to new skills and new technologies, and thereby visualising the competence improvement. Step three involves learning. Since learning is intangible and cannot be identified in a framework, we want to discuss the essence of what they have learned and why they have learned this by means of a box summarising the added learning. Step 1 - 3 are repeated according to the number of intermediate phases. In our illustration, there are three phases, thus the identification process is repeated three times. If it is possible to identify a leading skill and technology in the process development, it should be centrally positioned in process illustration to illustrate the relative importance between skills and technologies. In figure (XXvisualiserignen ovenforxx) skill C and technology 4 are centrally placed, and appear in all 63


Process innovation in Novozymes

phases thus illustrating their relative importance. The illustration appearing from the identification is considered a first draft, and the objective is to identify as many constituents as possible. We want discuss the essence of what they have learned and shy they have lerarned this as summarised in the boxes

5.4.4 Phase 4: Selection and prioritisation The selection and prioritisation lasts between 30 minutes and 1 hour, and consists of a

discussion of the most vital skills and technologies in the different phases on the map. Each constituent is evaluated, and the relative importance of different skill and technology areas are discussed. Some skills and some technologies are repeated several times during the overall process. This is an indication of a group of employees and a certain technology or a system of technologies that play a significant role in the process innovation. For instance in figure (XXvisualiserignen ovenforxx) technology 7 appears in phase II and reappears further integrated with technology 4 and skill C and G in phase III on the map. The selection and prioritisation analysis can thereby be used to identify the roles and importance of different skills and technologies, i.e. the trajectory and evolution of skills and technologies. Implicitly a selection process is performed to screen out the constituents that are not central to the development process. This way we assure that the process illustration only includes constituents that have played a significant role in the competence development. 5.4.5 Phase 5: Review process The review process can be comprehensive but should last no more than 15 – 30 minutes. The

objective is to evaluate the constituents and their relation to the current core competence of the department in which the research is carried out. Most favourable, the end result is a visual and somewhat codified explanation of what we have learned, why did we learn this, how do our skills and technologies link up to our core competence, i.e. which groups of skills and which technologies play a crucial part in the process innovation. The core competence identified in the pre-workshop preparation is re-evaluated in relation to the constituents to justify its importance.

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6 EMPIRICAL CASE PRESENTATION In this chapter we present the empirical case. The chapter serves as a building block in the subsequent analysis where we apply the framework to the empirical case. We have structured the chapter into four sections. First we introduce the case company followed by a presentation of the department we focus on. In the third section we present the product development processes in 1999 and 2004, respectively, and according to the pre-workshop preparation phase of the framework we introduced in section 5.4.1. Finally, we present the core competence of the department.

6.1 Novozymes A/S Novozymes has been chosen as our case company due to numerous reasons, of which we will elaborate a few. Being a world leader within a competitive and infant industry such as the biotech-based enzymes and micro-organisms industry has required a sustained effort to innovate and market truly new products through a strong focus on applying knowledge in developing and utilising technology in its processes and products. Due to its size, Novozymes provides a multi-faceted organisational context in which innovation takes place, and because of a divisionalised organisation, it has been possible to subject a single unit to a thorough analysis. We have chosen the protease department. Novozymes has been highly successful so far with annual revenues of DKK 5.8 billion in 2004. However, maintaining this position is a constantly challenging task, of both organisational and technological character. In the following we will briefly describe some facts about Novozymes. Novozymes A/S was established in November 2000 de-merging from Novo Nordisk A/S. Subsequently, Novozymes was quoted on the Copenhagen Stock Exchange in the year 2000. Novozymes manufactures approx. 75 types of enzymes and with almost 600 Detergent 36%

different products they have the largest

Technical 24%

enzyme product portfolio in the world.

Food 26% Feed 10%

Customers are not typical end-users,

Micro 4%

but

large

corporations

such

as

American-based Proctor & Gamble and

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Process innovation in Novozymes

British-based Unilever, which are both major players in the detergent industry. Novozymes’ enzyme solutions cover more than 20 different industries where the main industries are the technical (detergent, textile, leather and the forest industry), food (baking, juice & wine, alcohol and oils & fats) and animal feed sectors (animal feed industry).

When inquiring about enzymes in the dictionary they are “any of various proteins, as pepsin, originating from living cells and capable of producing certain chemical changes in organic substances by catalytic action, as in digestion“. They are naturally occurring. Enzymes can thus function as a catalyst in chemical processes without being destroyed during the process. In the human body for example, enzymes in the stomach brake down food to smaller pieces ready to be transformed into energy for the body. When the enzymes have completed their tasks they are biologically broken down. Enzymes produced by Novozymes are as indicated above used in a number of industries. In the detergent industry, for example, the enzymes enable stain removal at low washing temperatures and for preservation of colours of the fabric etc.

There are approximately 3900 employees represented in 25 countries of which 2000 are based in Denmark. The R&D facilities consist of approximately 760 people situated in Denmark, USA, Japan and China, the Danish site being the most extensive. In 2003 Novozymes had a global market share of approximately 44% within enzymes in general and export accounted for roughly 98% of the total turnover. However, they have experienced increased competition on the global market for biotech-based enzymes in recent years, their main competitors being the American-based Genencore and several Japanese companies. Despite this, Novozymes has retained a market share of 45-50% in 2003, within their core competence, being the detergent industry, compared to Genencore’s share of 36%, which secures them the position as the largest enzymes manufacturer in the world. With a very few exceptions Novozymes has been behind every major discovery in the field of enzymes for the last 40 years.

In 2003 Novozymes used approximately 13% of net turnover on R&D and have introduced 41 new products to the market during the period 1998-2003. In 2003, sales of new products and concepts accounted for around one third of total turnover and a dedicated effort in R&D is expected to ensure that this pattern can be sustained and further improved. This reflects the

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significance of the R&D department and their ability to continuously develop new products to the enzyme market.

6.2 Protease discovery department As mentioned above the highly divisionalised organisational structure enables us to subject a single unit to a thorough analysis. We have chosen to focus on the protease discovery department for a number of reasons, but before we provide a detailed explanation for our choice we first present Novozymes’ Innovation Funnel. The innovation Funnel provides the reader with a general overview of the R&D processes in Novozymes and thereby the ability to place the protease discovery department compared to other R&D activities.

The Innovation Funnel is a conceptual tool that illustrates the innovation activities divided in four phases. Prior to entering the innovation funnel, the idea-generation stage within Novozymes is a highly important initiator. When a given idea is approved by management it acquires the status as a new lead. In New lead the business prospects are considered by Industrial Strategy Group (ISG), Research & Development Management (RDM), Patent Portfolio Group (PPG), and top management to determine whether to incorporate this product in future research portfolio. If the product passes the strategic and technological evaluation it enters the discovery phase, where thousands of different associated enzymes are analysed to find the enzyme with the right characteristics. The third phase of the innovation funnel is development, which in practice is cleansing, fermentation and up scaling of the enzyme production. Since the enzyme has only been produced in small amounts in the laboratory, Novozymes needs to know whether up scaling of the production process to reach market demand is possible. Finally the product is launched, including marketing and other market oriented activities. An important part is the customer feedback which triggers new instances of idea generation and improved collaboration.

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The protease discovery department is part of the discovery phase and focuses entirely on screening enzymes for the detergent industry. The department consists of two smaller departments, micro biology (MB) and applied discovery unit (ADU). MB involves genetic engineering and high throughput screening whereas ADU involves application screening and testing and together the departments employ approximately 80 persons. As mentioned in the presentation of the innovation funnel, the objective of the discovery phase is to screen several thousand potential enzymes to identify an enzyme with the right characteristics. Hence the objective for the protease discovery department is to screen enzymes for the detergent industry and for this purpose they use number of screening technologies. Historically, screening for the right enzyme is a complex and time consuming task and it is a long process to fine-tune a successful technique. The protease discovery department has, as previously mentioned, succeeded in achieving an immense process development capability with the screening technology they have applied in the past four years. In itself this is quite a significant achievement, but evaluated relative to other departments focusing on other enzymes in the discovery phase, the process development is even more stunning. Therefore we focus on the successful process development in the protease department. As the reader may recall from chapter 5 when we presented the framework, a number of steps must be performed to establish the best possible foundation for the workshop. One of them is to thoroughly describe the screening processes before and after the process development, which will illustrate the process development over time and thus that the screening competence in the department has improved over the four year period. Below we describe the screening processes in 1999 and 2004, respectively.

6.3 Protease screening process The key to success in the discovery phase is the ability to discover the exact enzyme that has the correct genetic structure and stability to fulfil its purpose and satisfy customer needs. When it is found it will eventually be up-scaled, developed, optimized and sold to the customer. However, the process is time consuming and extremely costly. Previously, the chance of success was highly serendipitous and as a consequence the processes have been developed and optimised over the past 4 years. In 1999 it took on average of 10 months to screen for a new enzyme whereas today the same process takes on average of 5 months.

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6.3.1 Screening process in 1999 When Novozymes is faced with the task of finding the exact enzyme matching certain

performance criteria, a series of highly standardized processes is initiated. 30 years of experience and sophisticated knowledge about genetic structure of the protein is giving Novozymes a considerable head start compared to competitors. However, the project group only has tentative ideas of how this protease (enzyme) is supposed to be expressed. More than 60 million different proteins exist in Novozymes’ overall enzyme library, and only few have the potential to solve customer needs. The discovery process is an iterative process consisting of five steps. The first two are carried out by MB and are partially automated whereas the last three, carried out by ADU are manual.

The first step in MB is genetic engineering of the DNA inserted in a micro organism. The micro organism is then developed in a machine producing anything between 5 and 10 million different proteases in a liquid substance, all slight variations of the original protein. The process per se is common knowledge in the industry; and based on generic technology. However, the genetic engineering of the DNA is based on past experience of what works well, thus learning economies plays an important part. The first fully automatic screening process reduces the number of proteases from 10 million to 1 million, based on the protease’s activity and growth factor.

The second step in MB reduces proteases from 1 million to 100.000 by using a certain chemical called FITC (please refer to appendix 14.2). The chemical illuminates when interacting with the most active proteases, from where they are selected. The disadvantage of the technology is the lack of clarity regarding the quality of the proteases. The problem is that there are two parameters in activity, protease quality and molecular concentration. Proteases can either show up active due to high quality or high concentration. This means that proteases of poor quality can show up active and hence be selected, due to a high concentration of molecules. Consequently, there are both valuable and invaluable proteases among the 100.000 identified.

ADU performs the third step and randomly selects 500 from the 100.000 most active proteases identified in step two. Due to uncertainty regarding reasons for the high activity level, it is impossible with any degree of certainty to only select high quality proteases. To

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reduce uncertainty and exclude weak proteases, the remaining 500 are tested in microtit (please refer to appendix 1.2) with detergent and cloth with stains such as blood, grass, dirt etc. The microtit test is indeed small scale, with only 250 microlitre of each protease. The test reveals which proteases are best at stain removal and unaffected by the chemical composition of the detergent, within which it is embedded. The 10 best performing proteases continue to the next phase.

The forth step is mini wash (please refer to appendix 14.2), which is very similar to microtit wash just more comprehensive and larger scale, 50 millilitre. During mini wash test ADU selects only 2 from the 10 remaining proteases from the previous step.

The fifth step is a full scale washing test (please refer to appendix 14.2) of the two remaining proteases, which involves a normal washing machine, clothes with different kinds of stains and detergents and water quality. Based on the performance of the final test another round of the five steps may be initiated on the molecule structure of the best protease. This process continues until a protease with the correct characteristics is identified.

Along side the physical screening in mini wash, advanced computer technology is used to perform a virtual 3D test of the promising proteases. Computer guided screening is used to test the proteases’ molecular stability. Computer scientists use advanced software to visualize the genetic structures and mathematically manipulate the proteases to achieve higher stability levels. Scientists attempt genetically to change the structure. If the protease otherwise performs promising and if the genetic manipulation is successful a few options open up. A new round of the five steps can be initiated based on the more stabile protease or the new and more stabile protease is directly tested in mini wash.

To get an overview of the screening process in 1999 we have visualized the five steps and the feedback loops.

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MB MB ADU

Input: Diversified library

ADU <2

10 500 100.000

Minivash

Full scale

Customer ~1 Full scale

FITC

Primary Screening

6.3.2 Screening process 2004 The first step is very similar to that of 1999. Neither genetic engineering nor the first fully

automated screening reducing the number of protease candidates to 1 million, has changed dramatically. Nevertheless, Novozymes has a much better understanding of their enzyme library, a prerequisite for successful genetic engineering. The better starting condition the earlier they can identify an enzyme with the correct characteristics.

In the second step however, new technology has been implemented to replace the insufficient FITC technology. AMSA (please refer to appendix 14.2) is a new and fully automated machine that can screen 10.000 proteases per week and overcome the concentration problem described in the previous section. AMSA offers a significantly optimized quantitative and qualitative screening method. The new screening technology makes the protease discovery department overcome the problem of randomly having to select 500 out of 100.000 potential enzymes.

In step three the microtit technology is abandoned and replaced by AMSA. Microtit screening was previously performed manually whereas AMSA is fully automated. AMSAâ&#x20AC;&#x2122;s automation and flexibility means that more candidates are tested on more parameters such as stains,

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detergents, water quality. By implementing AMSA ADU has achieved a significant quantity and quality improvement.

Fundamentally, mini wash in step four has not changed, but it has been optimized on two dimensions. Quality and capacity has been optimized due to improvement of the AST method (please refer to appendix 14.2) and large scale purification (please refer to appendix 14.2). We find it important to note that the improvement of step three significantly blurs the previously sharp line between microtit screening and mini wash, so today it can be difficult to distinguish between the kind of screening carried out by respectively AMSA and mini wash.

The full scale wash technology has not changed over the years but is significantly more important today and has achieved a central role in the entire product development process. Vast amounts of data from full scale wash are used to verify the correlation between the different screening phases providing a valuable benchmark for new screening technologies.

The use of computer guided screening has also increased even though the fundamental characteristics of the technology are unchanged. As knowledge of different protease molecular structures increase, the use of computer guided screening will improve because scientists can directly relate cause and effect relationships in molecular structures

To get an overview of the screening process in 2004 we have visualized the five steps, the two departments, computer guided screening, and feedback loops.

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Process innovation in Novozymes

MB MB ADU

10 10.000 100.000 Micropurification AMSA

AMSA and Minivash

ADU <2 Full scale

Full scale

Primary Screening

6.4 Core competence of Protease Discovery department This section serves to identify and present the core competence of the protease discovery department. We have in cooperation with leading employees in the protease discovery department identified the following core competence:

“…to screen under as realistic conditions as possible while maintaining high through- put and high quality assays”

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Process innovation in Novozymes

Through-put

Application relevance

Reproducibility

The core competence we have identified refers to finding the right enzyme as fast as possible and is as such the core competence that allows Novozymes to continuously market pioneering enzymes at a fast pace and thereby fulfilling customer needs.

The core competence can be illustrated by the ‘competence triangle’. The illustration demonstrates the challenge in finding a compromise between through-put, reproducibility and application relevance to optimize the product development process. There is a trade-off between high through-put, high reproducibility and high application relevance, so the challenge is to strike the optimal balance between the three factors in order to ensure that the department develops enzymes with the correct characteristics as fast as possible.

For the purpose of this thesis we do, however not intend to subject the core competence to a thorough analysis. Instead of analysing the core competence as identified above, we examine the processes that support the core competence, because the core competence is a function of the underlying screening technologies supporting each of the three elements in the ‘competence triangle’. In other words, it is the development of high quality screening technologies that allows the protease discovery department to continuously “…perform screening under as realistic conditions as possible, while maintaining a high through-put and quality assays”. Thus we focus on the development of new screening technologies, i.e. process innovation.

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Process innovation in Novozymes

7 PRESENTATION AND EXPLANATION OF THE EMPIRICAL FINDINGS Chapter 4 and 5 answered sub-question 1A and 1B by developing the methodology and framework to identify constituents in the process innovation in the protease department. The previous chapter presented the empirical case and in this chapter the three chapters are combined to answer research question 1. In this chapter the methodology and framework are applied to the empirical to answer research question 1 by identifying what are the constituents of competencies leading to successful process innovation in the protease discovery department. The chapter is divided into three sections. First, the course of the workshop is discussed. Here it became evident that the design of the methodology had to be slightly modified to make it work as intended. Second, research question 1 is answered by presenting constituent of competencies lading to successful process innovation in the protease discovery department in the process illustration and discussing the different constituents and their linkages. Third, based on a model, the relationship between the core competence, product innovation and process innovation in the protease department is discussed. Further the sustainability of the core competence in the protease department is examined from a competence-based perspective.

7.1 Discussion of the workshop process In this section we analyse the actual workshop process and discuss whether or not it was carried out as we had planned or deviated from what we initially anticipated. Chapter 5 presented how the framework was designed based on a number of aspects we took into account. However, during the actual workshop process we experienced that certain aspects of our design lacked transparency and as a consequence we adjusted the original design slightly to match our anticipations with reality. Adjustments were made in the pre-workshop preparation phase as well as during the practical identification. Below these adjustments and implications are briefly discussed. 7.1.1 Pre-workshop During the different steps in the pre-workshop phase we realized the need to slightly modify

the design of the map. As our understanding of protease discovery deepened we became aware that the MB and ADU departments were clearly divided and it was therefore necessary to visualise this on the map. The reason for such division lies in the need to illustrate each

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department’s development processes including which technologies and skills are applied. Further, it was essential to clearly illustrate intra-departmental integration and coordination mechanisms. We believe this can be achieved with a sufficient level of transparency, by tailoring the map and dividing it horizontally between MB and ADU. In this way, lines crossing the horizontal centre of the map illustrate intra-departmental integration and coordination. Please refer to appendix 14.3 for an illustration. 7.1.2 Practical identification During the identification phase a number of issues were identified. This section illuminates

the areas that lacked clarity from the participants’ perspective and hence which areas and issues required adjustment during the process and in the subsequent analysis. Regarding the process illustration it was initially anticipated that a continuous and coherent picture of the development process would emerge. However, this did not materialise. Rather the emerging picture was a number of individual optimization processes alongside the central development of AMSA that amalgamated on various occasions during the five years.

The initial idea that the intermediate stages should represent central milestones forming the technology trajectory, through which the processes would run, turned out to be unrealistic because reality did not support this view. However, the milestones worked as a good way to visually and mentally break-up the development process into smaller more manageable phases.

During the identification process participants found it difficult to distinguish skills and technologies. The general conception among participants was that a given technology was associated with a skill or vice versa, i.e. the skills represented specific knowledge about a technology. Nevertheless, participants managed to identify and separate essential skills from technologies, which obviously proved valuable to the process illustration and comprehension of the process.

Another aspect that needed clarification was the result of two types of integration that appeared. Integration between technologies and between technologies and skills could either lead to development of a new technology or skill, or optimisation of existing ones. We found it necessary to distinguish between the two. The solution was to mark “leads to” when a combination of technologies or technologies and skills led to a new technology or skill. 76


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Similarly, external pressure was color-coded black to differentiate it from integration and coordination.

Initially it was believed that an important part of our methodology would be to emphasize technologies and skills. The participants found it extremely difficult to rate individual technologies and skills against each other without parameters to guide such rating. We acknowledged this and instead, based on participantâ&#x20AC;&#x2122;s consensus, selected and rated important clusters of technologies and skills throughout the development process.

Finally, the initial focus on tangibles was revised to improve the operational applicability of the framework. On several occasions during the workshop, the participants attributed aspects of the development process to intangibles, especially culture. However, in line with previous reasoning, this constituent was excluded from the mapping, as culture is not included as a theoretical element.

7.2 Presentation and analysis of workshop results This section serves to present the finding from applying the methodology and framework to the empirical case. Findings are presented in a visualisation in appendix 14.3. We urge the reader to unfold the illustration and use it as reference during this section to maintain breadth of view and ease of understanding. This is to ensure that the process illustration representing the workshop results is not confusing to outsiders and that the complex nature of both the product and process innovation is comprehensible. 7.2.1 Overview of the process illustration The illustration in appendix 14.3 visualizes sequentially the development process from 1999

and 2004. It provides a codification of skills, technologies, coordination, and integration mechanisms. The exact position of the constituents does not fully correspond to the time they actually occurred but the relative occurrence is realistic. To provide the reader with an overview of the illustration and reduce confusion this section serves to present the results of the workshop. The presentation is divided into six activities, each influencing the creation of the current process innovation; that is the current core competence.

The central technology in the process development is AMSA screening. The first version was introduced in 2000 and has been continuously upgraded. The invention of AMSA caused a

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growing need for developing and upgrading support technologies such as AST, micro and large scale purification, mini wash, and the automation technology. Underpinning the five year process development is a significant optimization of full scale wash, which has had a dominant influence on the protease department’s success. Finally on the organizational level it is worth mentioning that the previously distinct departments, MB and ADU over the years have developed close cooperation and internal understanding leading to significant interdepartmental knowledge transfer. 7.2.2

Activity 1

This activity concentrates on the invention of the first version of AMSA and is visualized in the upper left corner of the illustration. As illustrated in the framework, a number of things impacted the invention. The external pressure from a competitor who suddenly introduced a new superior screening method forced Novozymes to think in terms of new innovative screening technologies to stay competitive. Further, input from a customer visit inspired the chemical engineer MAS to initiate a project to incorporate pioneering screening principles that he had seen at the customer’s test facility into a new screening technology. He capitalized on the idea by assigning technical engineer SNK and the chemical engineer VISN to the task of developing a prototype of a new screening technology based on customer input. Positive prototype test results led to the development of AMSA 1 in cooperation with Proinvent, an external mechanical engineering company. The first version of AMSA was according to SFFE, manager in MB “…rather primitive and operated manually”.

From a knowledge perspective MAS activates deployment knowledge to initiate the AMSA projects while his integrative knowledge is used to integrate the specific knowledge bases possessed by SNK and VISN. By possessing deployment knowledge and integrative knowledge, MAS has had enormous importance for the instigation of the AMSA project. 7.2.3

Activity 2

The second activity is depicted in the lower left corner of the illustration and concerns full scale wash and its impact on the process development.1 Full scale wash plays a significant 1

The key to screen potential enzymes optimally is to do so under conditions as close to the end-user as possible. This is done by testing the potential enzyme in full scale washing machines. The problem is that this is a costly and time-consuming process, which is only carried out on a few highly qualified enzymes. Preferably this type of screening is totally excluded in the process however as it serves as the ultimate test of product quality it is today an unavoidable part of the screening process.

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role in the entire screening process because it is regarded as the ultimate screening test in the sense that the enzyme’s quality can be minutely examined. This kind of testing is extremely costly because it is performed manually and therefore very labour intensive. Hence the full scale lab in China was initially established to reduce costs and increase capacity. Running a sophisticated and well-functioning full scale washing lab has proved to be a vital factor underpinning the entire process development. The ability to extract large amounts of useful data and compare it to screening processes in earlier phases in MB and ADU assured correlation between the different screening technologies. In this sense the comparison of data would serve as a guide for the quality by validating the test results obtained from the individual screening processes in MB and ADU. Statistical programs were developed specifically to facilitate extraction of useful data and handle large amount of data from the test lab, which in turn allowed ADU to better compare test results internally. A fine-tuning of full scale washing was achieved by performing tests together with a customer to compare test results. This propelled validation of the test results and provided a valuable benchmark for other screening technologies. Over the entire 5 year period statistical programs and colocation tests with customers continuously improved thereby optimising the data quality from full scale wash. Full scale wash has therefore played a significant role during the entire process development. 7.2.4

Activity 3

The third activity covers the development from ASMA 1 to ASMA 2a in MB and is visualized in the upper central part of the illustration. We will focus on three areas: increase of AMSA’s capacity, automation of AMSA, and development and upgrade of supporting technologies. The difference between AMSA 1 and AMSA 2a constitutes a major technological development that can be attributed to a fruitful cooperation between Proinvent and Novozymes personified by SFFE. SFFE has played a major role in the development of AMSA version 2a. During the development process there was internal doubt whether or not the project was viable, but SFFE’s deployment knowledge was apparent to the organisation and his commitment had a positive effect on the AMSA project.

AMSA 2a increased screening capacity by a factor four moving from first to second generation and the screening quality also increased dramatically. Another vital difference

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between the two generations is the change from manual operation of AMSA to almost full automation. The automation expert GEAB, implemented a pipette robot to automate the process of feeding AMSA with proteases. To exploit the potential of the AMSA technology there was a need to develop and upgrade supporting technologies. As SFFE expressed it “...without a similar development of supporting technologies using AMSA would be like driving a Ferrari in the centre of Copenhagen”. Another area in need of improvement was purification and the technology was markedly optimized. PQRS, the purification expert received input from a conference and developed a prototype of a new micro purification technology. This development gave rise to a subsequent development of large scale purification. Finally the protein engineer, SMIN managed to optimize the fermentation technology to increase the number initial number of enzymes feeding into AMSA. 7.2.5

Activity 4

This activity focuses on a single screening technology in ADU and is visualized in the middle/lower central part of the Illustration. The optimization of mini wash is interesting because it is more a result of optimizing other technologies feeding into mini wash than optimizing mini wash itself. The capacity of mini wash increased due to the development of the large scale purification technology and the optimized fermentation technology, both described above. Further, an improved AST method, input from SFFE, SMIN, LLHC, and KVJO along with valuable benchmark data from full scale wash increased mini wash capacity to accommodate the growing number of hits delivered by AMSA. 7.2.6

Activity 5

Activity 5 is depicted in the right part of the illustration. We focus on three areas: development and implementation of AMSA 3b in ADU, development of the automated purification robot and the general implementation of AMSA in MB and ADU, i.e. the current situation. AMSA 3a was developed approximately one and a half year after AMSA 2a in MB. This does not imply that ADU did not use AMSA prior to developing AMSA 3a but merely that MB’s AMSA 2a was used without customising it to fit ADU needs. The assay expert2 JUKN has been the pivotal point in the development of AMSA 3a and has obviously benefited from the experience accumulated during the development of AMSA 2a in MB. Further JUKN and GEAB have developed pipette automation technology for AMSA 2b,

2

The assay expert is a specialist in testing proteases under various conditions

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which has also supplemented the pipette knowledge in MB. The intense work with automation technologies across departments helped GEAB to capitalize on his knowledge base and develop a micro purification robot.

From a knowledge perspective, the coordination between JUKN and GEAB is an interesting observation. Both would be characterised to possess specific knowledge, but especially JUKN has been a key person in developing AMSA version 3a in ADU. This means that along with a deep specific assay knowledge he also activated integration knowledge by using GEABâ&#x20AC;&#x2122;s specific knowledge to develop AMSA version 3a.

The status of the development process is that AMSA versions 2b and 3b are fully implemented in MB and ADU, respectively. Fully implemented implies that AMSA tests correlates with test data from full scale wash, but not that the process development is complete. There is still room for improvement and optimization of one technology leads to other optimization needs. 7.2.7

Activity 6

Contrary to the five activities previously described, this one covers the entire development process since it concerns coordination and integration mechanisms. To maintain an overview we focus on a few pivotal areas. In the central left part of the illustration coordination between the project leader from ADU, the head of the screening in MB, and the computer protein designer from MB together increased the mutual understanding of the processes in each department and hence they have demonstrated integration knowledge.

Historically those departments were working autonomously, which reduced the chance of successful product development. However, initiatives to increase cooperation caused key personnel to start sharing knowledge and to a large extend these new integrated knowledge domains optimised the product development processes. Another example of successful coordination refers to what was touched upon in activity 4. As visualized in the process illustration, the coordination between SFFE, SMIN, LLHC, and KVJO is very central in the development and integration of several technologies. Finally, in 2002 it was decided that some employees should start on job rotations to gain insight into other research areas across the two departments. Altogether, the initiatives brought about higher cross functional

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relationships and mutual understanding of diverse processes, which has proved crucial to the process development today.

Even though we have presented the activities individually they are obviously inter-linked, which the illustration also confirms. As noted from the illustration there are several coordination and integration mechanisms across the two departments and between technologies and skills. We believe that the process illustration has codified the process innovation and provided a fundamental understanding. As we mentioned in the introduction to this chapter, we perceive the process illustration as the answer to research question 1 what are the constituents of competencies leading to successful process innovation in the protease discovery department. There are obviously constituents that have had a more profound impact on the process development than others but in all the constituents presented in the process illustration have led to the successful process innovation.

7.3 The relationship between core competence, product- and process innovation So far, in section 6.3, we have separately presented the product development processes in 1999 and 2004 respectively, and the six activities representing the constituents in the process development from 1999 to 2004. In this section we will demonstrate the relationship between product- and process development and the core competence to elucidate the iterative process development. We find such analysis valuable in raising the overall level of abstraction and to provide the reader with an overview of the linkages in the empirical case. Furthermore, we will discuss the sustainability of the core competence as this is crucial to maintaining competitive advantage and continuous success in Novozymes. Please refer to the illustrative model presented in figure (xxXXxx) 7.3.1 The core competence in context The model consists of three parts. The first box, competence, includes the skills, technologies, and organisation that were identified during the workshop. These constituents feed into the second and central part of the model, which consists of the process development, product development and problem solving. In essence this part of the model is an abstraction and elucidation of how the process innovation developed over time and demonstrates that the illustration is a continuous process development feeding into the product development as technology matures and problems are solved. The result of the process is then illustrated in the competence triangle that moves to a higher level to symbolise the competence development.

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The three elements in the second part of the model are inter-linked by the iteration process that occurs between them. Overall, the process innovation arises from the need to solve problems in the development of new products. This could for instance be a bottleneck in a screening technology, which delay the entire product development process, and simply perform at a suboptimal level. When such bottleneck is identified a process development project is initiated to solve the problem and hopefully a new technology can be developed and implemented in the product development. In turn, new product development processes may cause other bottlenecks to occur and the process cycle continues. The process illustration of the process innovation reveals such iteration and ties the product- and process developments together.

By applying the model to the empirical case a clear relation between product- and process developments becomes clear. The search for a new screening technology in 1999 arose due to several reasons. The main reason being MBâ&#x20AC;&#x2122;s loss of potentially valuable enzymes due to random selection after the second screening3. The AMSA project was initiated based on needs arising from the 1999 product development process and AMSA was developed and implemented into the product development process in 2001. The introduction of AMSA gave rise to a number of new bottlenecks, because other technologies and processes had to be optimized as well as to utilize the full potential offered by AMSA. Hence the iteration process was initiated again and technologies such as micro- and large scale purification, AST, and fermentation processes were developed or optimized and implemented into the product development process. The iteration process has continued like a spiral solving more and more problems by developing new technologies that have been implemented into the product development along the way.

The result of the development process is illustrated in the â&#x20AC;&#x2DC;competence triangleâ&#x20AC;&#x2122; in the last step of the model. When we introduced the competence triangle in the preceding chapter it was argued that it visualises the core competence of the protease discovery department. Combined in this model the purpose is further to illustrate the competence development, following the iteration process between the product and process development. The competence development is illustrated by a shift in the level of the competence triangle, i.e. the purpose is to illustrate that the competence has developed over time. Following this rationale means that during the process innovation from 1999 to 2004 the competence developed significantly.

3

Please refer to section 6.3.1 for a detailed description of the product development process in 1999

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7.3.2 Sustainability of the core competence In the previous chapter we presented the core competence of the protease discovery department as “…screening under as realistic conditions as possible while maintaining high through-put and high quality assays”. We argued that the core competence was a function of the underlying screening technologies supporting each of the three elements in the ‘competence triangle’. The argument owes to the fact that the ability to “…to screen under as realistic conditions as possible while maintaining high through-put and high quality assays” is directly derived from how advanced the available screening technologies are. From the relationship between the core competence and the supporting screening technologies arises an interesting discussion of whether or not the core competence is sustainable. If not, the success of the department and the competitive advantage in Novozymes could be jeopardized. According to Hamel (1994) sustainability is achieved through inimitability of skills and technologies, and following this line of argument, we now analyse if the protease discovery department’s core competence is sustainable.

The constituents codified in the process illustration each play a significant role in building the core competence. When examining the individual skills and technologies only few are likely to be inimitable. In chapter 3, a distinction between generic and customised technologies and the 3 types of skills was made. Here the distinction is used to elucidate the level of inimitability of the core competence.

Both generic and customised technologies form part of the product development, and it seems fair to state that generic technologies, accessible to competitors on the open market, individually do not offer any inimitability. Customised technologies, on the other hand, may offer a valuable inimitable role to the core competence. AMSA is an example of a customized technology, which may be difficult to imitate because it is protected under the patent law. However, AMSA is a system of readily available generic components, and consequently it may be possible for competitors to circumvent the patent and develop a technology with similar screening capabilities. Most of the other technologies that were identified in the process innovation are generic or only slightly customized and therefore, it is argued that the individual technologies in the process innovation at least to some degree are imitable. According to Hamel (1994) this is not surprising, because inimitability does not occur from an individual technology, but rather from a system or integration of technologies and skills.

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This brings us to rely on skills, the second element in inimitability. In general skills are difficult to imitate, but it largely depends on the type of skill and knowledge. Specific knowledge offers the lowest degree of inimitability. It would for instance be practicable to replace a computer scientist or a chemist with the same or similar specific knowledge about a particular process or technology. For example, Genencore, the prime competitor of Novozymes, could fairly easily employ the exact employee or an employee with similar qualifications. Contrary, both integrative and deployment knowledge occur in a context or a system of skills and technologies. These types of skills are somewhat difficult to imitate because the value of the skill depends on the context in which they occur and therefore the value of the skill is lost, if positioned in a new context. However, most individual competencies can be acquired over time.

Based on the analysis above it is ambiguous whether the core competence of the protease discovery department is inimitable. Only one of the technologies in the process illustration is protected by patent law and maintains a degree of inimitability, while the rest of the technologies are imitable due to their generic character. Contrary to technologies, skills are relatively difficult to imitate, and therefore it is hard to conclude if the core competence is inimitable. Conclusively, it is primarily the systems of skills and technologies and the way they are coordinated and integrated in the organisation that determines their unique employment.

We will return to this discussion in the cognitive analysis, and compare the sustainability question with the competence-based view of sustainability.

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The framework can be perceived as a visualisation of the process continuously feeding into the product development as technology matures and problems are being solved.

Organisation

Technologies

Skills

Competence

Process innovation in Novozymes

Primary Screening

Problem solving

Input: Diversified library

MB

FITC

Bottle neck

100.000

Product development

AMSA

Process development

500

MB

Bottle neck

Minivash

10

ADU

Full scale

~1

Customer

Bottle neck

Full scale

<2

ADU

Reprod

86

The new technology boosts all thre dimensions and allow for both high thro put and high quality assays.

The challenge is to find the balance bet high through-put, application and reproducibility, and to develop the techn solving this problem.

Application

Through-put


Process innovation in Novozymes

PART III 8 A COGNITIVE FRAMEWORK FOR ANALYSIS In the preceding chapter we emphasised the overall empirical findings of the process development by answering research question 1. We are now going to present the theoretical framework that will be used to answer research questions 2 and 3 by analysing the cognitive aspects of the process development.

First we will present the â&#x20AC;&#x2DC;Information Spaceâ&#x20AC;&#x2122; by Boisot (1995) that will be used to analyse and explain how knowledge itself enables the department to better utilise and share knowledge. Behind the obvious processes in cooperation between groups and individuals in the department lies a profound dynamic process of making problem solving more effective. This is done through a process of codification, abstraction and diffusion that work to achieve a more effective utilisation of physical resource by economising on data processing.

Second we will present the cognitive model of innovation by Nightingale (1998) to analyse and explain how knowledge in the department is applied to the innovation process. Nightingale argues that scientific knowledge cannot be directly applied to innovation because innovation starts with a desired end-result but the starting condition to achieve such end-result is unknown. In other words, the process development in the protease department is a situation caught in the desire to develop better screening technologies for more efficient enzyme production, however the starting condition and process of how to achieve such end-result is unknown and only materialises as they advance in the process.

The objective of applying the Boisot and Nightingale to our case company is first of all to apprehend a cognitive understanding of how knowledge creation and knowledge exploitation are applied in the innovation process. We consider the two theoretical models as complementary. While Boisot explains how the learning process works to economise on data processing and develop new knowledge, Nightingale explains how such knowledge is applied and exploited in the innovation process. First of all this helps to understand the cognitive processes driving the current successful process innovation, moreover codification and abstraction reveal an underlying analytical process, which to our interpretation can be enhanced to further improve the development of new screening technologies.

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8.1 Boisot In what follows we wish to account for the theoretical concept attached to Boisot’s threedimensional model, the Information Space or I-space. This we find relevant as the model make up the foundation for our analysis and operationalisation of the empirical findings. The objective of the section ahead is to provide a deeper understanding of the elements that together establish our theoretical framework.

The I-space constitutes a conceptual tool that by means of the three dimensions: codification, abstraction and diffusion can be used to examine the production and transmission of information and knowledge in a social context.

A social context is a limited size; hence we can decide to perceive the I-space as the whole world, a company, or a separate

process

innovation

in

the

Codification

department. Since we are analysing the protease

department this will constitute the primary social context. However as we shall see the department is communicating with external actors

that

innovation.

influence

the

Abstraction

Diffusion

process Figur 2-2: Information Space (Boisot, 1995:166)

Boisot’s model is based on a revised economical agenda that view information and knowledge as more than just support for economical transactions. From the perspective of a knowledge based society it is the ability to create and exchange knowledge that is of the essence. When knowledge is no longer just support for economical transactions, but also driving the transaction it self, the traditional models for production resources like capital and labour do no longer explain the whole picture. Instead we need a model to understand the processes whereby knowledge creation and exploitation are effectuated, “… a framework for thinking about the relation between physical and knowledge assets.” (Boisot, 1998:17)

Knowledge creation and exploitation are mutually supportive. Knowledge creation leads to knowledge exploitation that again enables and necessitates knowledge creation. The

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Information Space explains the processes creating new knowledge and moreover what happens when such knowledge is exploited. It characterises the space in which knowledge develops. The model gives both a static picture of knowledge and information and a characteristic of their dynamic evolution. The model thereby applies to the general perception of organisational knowledge as both static and dynamic.

Before we commence explaining the I-space it self, we find it necessary to distinguish between data, information and knowledge. 8.1.1 Data, Information, Knowledge We take data to be discernable differences between states of the world (Lloyd, 2000, 2002)

i.e., hot/cold, dark/light, present/absent, etc. Such differences can ultimately be represented by binary digits, or bits, some of which will carry information. Information itself, however, is a relation between these discernable states and an observer. A given state is informative for someone. It may carry no information for his/her neighbour. Information is then what an observer will extract from data as a function of his/her expectations or prior knowledge (Boisot, 1998). Finally, knowledge can be perceived as dispositional. It is those prior beliefs on which we are willing to act. It consists of a set of probability distributions which orient our behaviour and which in turn are modified by incoming information extracted from data (Boisot, 2000:665).

It is important to notice that what the distinction brings out is that, in our daily interaction, whether being with objects or other people, it is only ever data that actually flows and get stored. Whether data is informative to us or other people depends on its relationship with our prior beliefs and knowledge base. Strictly speaking we are simply transmitting data and all we can hope for is to bring resonance between our own belief systems and those of other agents. If not, our data-transmission is lost in noise and confusion.

In a working environment data is transmitted in large volumes or small volumes. Given our limited time and capacity to receive, process, store, and transmit data, the objective is to economise on the rate of flow. One way of doing so is to structure the data whereby we can eliminate superfluities, - i.e. data that is uninformative. This way we can optimise how we share information and create knowledge, and ultimately even the speed at which we solve problems in e.g. process innovation. Boisotâ&#x20AC;&#x2122;s framework takes as its point of departure our 89


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need to economise on data processing and transmission efforts as well as the data structuring strategies that we adapt in response (Boisot, 1998). 8.1.2

The I-Space

"The I-Space is a conceptual framework within which the behavior of information flows can be explored and, through these, the creation and diffusion of knowledge within selected populations can be understood." (Boisot, 1998:55) The three dimensions codification, abstraction and diffusion make up the axis in the I-space. Therefore it is important to understand, what exactly lies behind those concepts, to make the subsequent analysis and operationalisation more meaningful.

The I-Space takes codification and abstraction as the two processes through which data is apprehended and structured. We will now clarify the two concepts.

Codification means reducing the amount of data necessary to create information. Through codification we sort out irrelevant data, i.e. noise, whereby the remaining data appear simpler and easier to express. Hence, codification makes it easier to express knowledge orally as well as in writing. Codification thereby sorts out and structures data into categories. In other words, codification allows us to economise on data-processing by providing more effective information by means of structuring the data necessary to comprehend information.

Codification facilitates the distinction between phenomena as well as between the categories to which these are assigned. Drawing on algorithmic information theory (Chaitin, 1974), codification can be measured by the amount of data processing required to perform a distinction either between two phenomena or between two categories.

To make information as useful as possible it is necessary to classify categories. By reducing the number of categories to comprehend a set of data, the complexity of information is minimised. Hence, abstraction assesses the number of categories required to apprehend a given phenomenon. The fewer categories needed, the more abstract is the classification scheme used. In other words, abstraction generalises data by minimising the number of categories from which data can be understood.

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Both codification and abstraction have the effect of economizing on an agent’s data processing and transmission resources. All things being equal, phenomena that are well codified and abstract require less data processing by agents than those that are uncodified and highly concrete – i.e. highly contextual. This assumes, of course that well codified classification schemes already exist. If they do not, then they must be created, a task that may well consume considerable quantities of data processing resources.

Well codified and abstract data economizes on both a senders’ and receivers’ data processing and transmission resources. For this reason, well codified and abstract data will diffuse to more agents per unit of time than uncodified and concrete data. This brings us to the third dimension of the I-space.

Diffusion constitutes the third axis in the I-space. When referring to diffusion it is important that we distinguish between the physical diffusion of information and the extent to which such information is captured, apprehended and implemented by the receiver. In relation to the Ispace the concept covers exclusively the extent to which information is made available to a small or large group being people, organisations or something else.

Naturally it is relevant whether or not the information is being apprehended by the receiver. There are three overall considerations to take into account in relation to diffusion (Boisot, 1998:53):

1) Is the message received the same as the message sent? 2) Is the message received understood? 3) Is the message received acted upon as intended?

Finally diffusion can “… be scaled to refer to the proportion of a given population of dataprocessing agents that can be reached with information operating at different degrees of codification and abstraction” (Boisot, 1998:52).

Conclusively, codification and abstraction work in tandem to enhance the cooperation between individuals or groups in an organisation. The better we understand our own field of

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activities as well as other’s, the better we become at producing and transmitting information and knowledge in a social context. 8.1.3 The Social Learning Cycle The objective of analysing the process innovation is furthermore to assess the level of

codification and abstraction at which knowledge creating processes are carried out. To do this it is relevant to take a closer look at Boisot’s definition and description of the Social Learning Cycle (SLC). First of all this will visualise the process innovation from a cognitive perspective and explain the learning mechanisms driving innovation over time. Secondly, we can use the framework to evaluate and illustrate the cognitive level at which innovation is carried out in the department.

“Knowledge can be located in the I-space as a function of its data processing characteristics. Over time, these are modified by learning. What starts out as a fuzzy intuition gradually gains in form and structure, that is, it becomes increasingly codified and abstract and hence moves up and towards the back of the I-space. When this happens, knowledge can be more easily shared. But learning can also move knowledge down the I-space as, for example, when we acquire well structured data that we gradually internalize through practise. Over time, codified and abstract data can develop a penumbra of uncodified and concrete insights, personal intuitions that reflect the history of our engagement with it. Such ‘experiential’ knowledge is not so easily shared”. (Boisot & Griffiths, 1999:667) The Social Learning Cycle is according to Boisot a cyclical movement in the I-space illustrating how knowledge is created, shared and utilised in a given population. The SLC is split into two overall phases being the value creating and the value exploiting processes. The two phases can be further devided into six different phases that we will now briefly present.

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4 5

Key: 1. Scanning

Codified

2. Problem solving

3

3. Abstraction 4. Diffusion

6

5. Absorption 2

Abstract Concrete

Uncodified 1

Diffused

6. Impacting

Undiffused

Figure 2-3: The I-space (Boisot, 1995) 1. Scanning – is a movement towards undiffusion. Data is localised in relation to a given field of interest. The process can be perceived as partly analogous to Nonaka’s socialisation where tacit knowledge (1995) is shared in a social context, i.e. a ‘learning by doing’ or ‘learning by using’ fashion.

2. Problem-solving – is a movement towards codification. The data recognised during scanning is given structure and coherence, i.e it is sought codified and turned into information. This enables the capacity for further data and information handling; meanwhile just codified data (information) becomes practicable.

3. Abstraction – means generalising the application of newly codified insights to a wider range of situations. This involves reducing them to their most essential features, i.e. conceptualising them. Problem solving and abstraction often work in tandem and can be compared to Nonaka’s externalisation where tacit knowledge is codified made explicit and abstract in thought.

4. Diffusion – a movement to the back of the I-space towards diffusion. The concepts being abstract and general information is shared either inside of outside the social system. Diffusion as well as abstraction is analogous to Nonaka’s combination during knowledge creation, where existing explicit knowledge is combined, i.e. made abstract, whereby knowledge is made general and shared amongst participants.

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5. Absorption – a movement towards uncodification. Information is absorbed by being incorporated in intuitive processes in a ‘learning by doing’ or ‘learning by using’ fashion, whereby, once again, capacity for individual data handling is released. The process is closely related to Nonaka’s internalisation, where explicit knowledge is embodied into tacit knowledge.

6. Impacting – is the embedding of abstract knowledge in concrete practises. Thereby, newly absorbed insights have an impact on new solutions through ‘learning by doing’. E.g. embedding can take place in artefacts, technical solution and new technologies. Absorption and impacting often work in tandem. It is difficult to directly compare impacting to the knowledge creating process by Nonaka, however it most likely compares to internalisation in that concrete knowledge is applied in new situations

The SLC can take many different shapes and it can take place over month or years depending on the characteristics of the learning process. Hence the SLC applies to both quick learning processes within hours or it takes place over years like the process innovation in the protease department, - it is independent of time.

The first three phases (scanning, problem-solving, abstraction) is where value is created and the last three (diffusion, absorption, impacting) is where the value is exploited.

The SLC can be used to identify the processes necessary to move information or knowledge from one level in the I-space to another. This way the I-space compares to an innovation process within a company that is necessary to bring the company from one level to the next, by creating knowledge from existing information and by exploiting already existing knowledge. New knowledge enters the I-space either when new agents enter the I-space or through the cyclical movement of existing knowledge in the space. Likewise the SLC applies to explain the processes necessary for two agents to exchange knowledge (Boisot, 1995:224).

In summary, the I-space theory is interesting in relation to the process innovation because it helps elucidating what is not obvious from a competence-based view. The I-space is useful to reveal the underlying cognitive methodology of how problems are processed, analysed and

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solved in the department. The I-space theory becomes even more interesting in combination with the cognitive model of innovation by Nightingale that we are now going to present.

8.2 Nightingale “… science cannot be directly applied to produce technology because science answers the wrong question. Innovation starts with a desired end result and attempts to find the unknown starting conditions that will achieve it. Scientific knowledge, by contrast, goes in the opposite direction, from known starting conditions to unknown results.” (Nightingale, 1998:689) The intriguing question in innovation is how companies manage to develop new products and new technologies when in fact the starting condition to achieve such desired end-result is often unknown. Much has been written about how companies create new knowledge (Nonaka, 1995; Boisot, 1995, 1998; Cook & Brown, 1999) and develop new competencies (Drejer, 2002). Nightingale theorise on how such scientific knowledge is used in innovation, and interestingly how it cannot be used. Nightingale does not question the value of creating new knowledge and developing new competencies, however his cognitive model of innovation (1998) questions whether it is simply strait forward knowledge possession that drives innovation processes. Instead he suggests a cognitive model that explains how the innovation process moves from an initial ill defined conception of a problem, through a series of subproblems, to a finished technology.

The model consists of three building blocks. The first is a cognitive theory of knowledge that considers knowledge as a capacity to extrapolate patterns. This capacity is both embodied in the biology of human brain and embedded in social networks, rather than being an abstract entity like information (Nightingale, 1998:690). The second is a simplistic conception of science as a social practise of mapping and codifying patterns in nature. Part of this involves exploring how three isomorphic levels of pattern, the mathematical, the physical and the imaginative, all interact and diverge from each other. Lastly, the third building block is a discussion of the nature of technology itself, where technology is defined in terms of ‘artificial function’. This argues that the functions that technologies perform are not intrinsic properties and are therefore dependent on tacit knowledge (Ibid).

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Before we go deeper in to the individual building blocks we would like to emphasise why this is interesting. Nightingale further argues, “… that theories [treating] the output of science as ‘information that can be directly applied to technical change’ are problematic because science answers the wrong question” (Nightingale, 1998:690). An innovation starts with a desire to develop a certain technology, i.e. the end result, and then tries to find the unknown starting conditions that will produce it. But, scientific knowledge cannot simply comprehend how to produce such technology because science can only go in the opposite direction, from known starting condition to unknown end results. If we take an example from the production of an enzyme for a particular purpose and for a given customer, Proctor & Gamble. If for example P&G needs an enzyme that can remove fat stains at 40°C but the exact enzyme to fulfil such purpose is unknown. Novozymes cannot start with a desire to produce such an enzyme, simply add biochemical arithmetic, 3D chemical structure engineering and some expert thinking and find a practical solution. This is Nightingale’s direction argument, and this is what interests us, since the protease department appears to succeed with such innovation anyway, and beyond comparison in the industry. Likewise, this accounts for the process innovation of developing new screening technologies. The starting condition of how to develop a new and better screening technology is also unknown. We will return to this in more detail in chapter 10.

Unknown starting conditions

Process Innovation

Desired end result

Known starting conditions

Scientific Knowledge

Unknown end result

Figur 2-4: The direction dilemma between process innovation and scientific knowledge

8.2.1 Building block 1: Knowledge as a cognitive process The first building block explores knowledge, and in particular how tacit knowledge is applied

in innovation. Tacit knowledge enables us to ‘see as’ rather than simply ‘see’, because we can

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actively interpret our experiences rather than passively receive information (Gregory, 1980, 1981:383). As such ‘perceptions are hypotheses’ because we perceive by relating information from our senses to a learnt background of experience to hypothesise a best fit (Gregory, 1980). Our background of experience stores as tacit knowledge that acts to fill in missing parts of understanding in new situations. Thus our background knowledge helps to solve incomplete patterns by relating previous experience to new situations and hypothesise the best solution to a given problem. This kind of background knowledge and experience is hard if not impossible to codify and transmit because it is the background to which codified transmitted information is compared (Nightingale, 1998:693).

Because biochemical processes in the brain link experiences to ‘similar’ memories we can extrapolate linkages and have a contextual understanding of what is likely to happen next, in Edelman’s phrase we live in the ‘remembered present’ (Ibid). Thus knowledge is the capacity to act on patterns in our experience that we have perceived by relating them to a learnt tacit background. In Novozymes this capacity is exercised when they properly recognise and extrapolate a pattern between similar chemical structures of enzymes and the application for different purposes. For example it is possible for the experienced chemical engineer to recognise patterns between certain chemical misbehaviour and what is wrong with the chemical structure. Likewise, it is possible to recognise patterns between mistakes in the screening technology and wrongly suggested enzymes. For example, some enzymes may prove valuable when tested in miniwash whereas it does not work in full scale wash when mixed different kinds of detergents. Nightingale cognitive process is illustrated below:

Patterns in World

Time

Future

Tacit understanding of patterns

Extrapolation

Prediction Expectation

Figure 2-5: Extrapolation through tacit understanding (Nightingale, 1998:694) Finally, tacit background knowledge in the protease department is moreover the capacity to interpret information and comprehend things that cannot be codified. This tacit knowledge

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cannot be codified and visualised in the framework but is rather visualised as the pattern described above. 8.2.2 Building block 2: Science as pattern Above we described the ability to extrapolate patterns simply through our tacit understanding.

The second building block extends the notion of knowledge being the ability to extrapolate patterns by exploring and codifying patterns in the behaviour of nature. What happens in practise is in fact recognition of patterns in nature that we compare with our tacit expectation of such behaviour and the mathematical patterns of the behaviour in nature. The key point is that these three levels of pattern are isomorphic, i.e. the same shape. Or to be more precise they are topologically equivalent (Nightingale, 1998:694).

Patterns in Nature

Tacit Understanding

Mathematical Patterns

Figure 2-6: Three levels of Pattern (Nightingale, 1998:694) Scientific knowledge comes from the ability to form an abstract correspondence between all three levels of pattern. This provides the capacity to explore patterns on one level and apply those patterns on another level.

Similar to Boisot’s ‘abstraction’, Nightingale (1998:695) argues, that the ability to perceive patterns reduces the amount of information needed to understand the world and hints at an underlying order that can be abstracted by recognising patterns. Barrow (1998, 1991) describes science using the metaphor of ‘information compression’:

“the goal of science is to make sense of the diversity of nature. It is not based upon observation alone. It employs observation to gather information about the world and to test predictions about how the world will react to new circumstances, but between these two procedures lies the heart of the scientific process …the transformation of lists of observational data into an abbreviated form by the 98


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recognition of patterns. The recognition of such a pattern allows the information context of the observed sequence of events to be replaced by a shorthand formula that possesses the same or almost the same information content”. (Barrow, 1991:210) This is similar to the process of codification and abstraction proposed by Boisot (1995, 1998). Scientists do not just assemble facts; they also find patterns in those facts, whereby they simplify complex phenomena to be easier comprehended and processed more efficiently.

Turro (1986:882) supports this view and adds that intellectual processing involves creating a “…stable and self consistent interpretation of a phenomena or event”. This is done by fitting data into pre-existing patterns, and secondly by recognising and generating new patterns.

The chief advantage of the view on science as pattern against the vast array of disparate information that makes up innovation, is the way in which a relatively small number of guiding principles can be used, not only to explain and interrelate existing facts, but to forecast the outcome of changing the conditions under which already known reactions are carried out, and to foretell the products that may be expected from new ones. Thus patterns can exist as guiding principles that explain and interrelate existing facts, and can be extrapolated to forecast and foretell the future (Skyes, 1986:1).

Nevertheless, when it comes to complex phenomena, the problem of viewing science as pattern is that the parameters affecting phenomena are not contained in the original laws of nature. “They are instead a result of historical symmetry breaking. As a consequence, a very important consequence, knowledge about what these parameters will be cannot be obtained from first principles. It has to be found by trial and error” (Nightingale, 1998:697). In other words, it becomes increasingly difficult to forecast phenomena as the complexity and amount of information increases, because the simple laws of nature generate symmetrically unstable outcomes that interact with their surroundings in unpredictable ways, which is why trial and error becomes indispensable. Consequently, “…when science is used to explain, it moves from concrete phenomena to abstracted patterns in its behaviour, leaving behind all the symmetry breaking information that makes any situation specific. But, when it is used for prediction this extra information is needed, and is not contained in the original laws” (Ibid).

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The clarification above is important to understand the difference between science and technology. It explains why technology cannot be the extrapolation of scientific patterns into the future because; symmetry breaking precludes direct pattern extrapolation. I.e. abstracted scientific patterns in technology innovation cannot be applied to new concrete technology innovations or process innovations, whereby forecasting the future becomes uncertain. 8.2.3 Building block 3: Technology as artificial function “Because innovation involves moving ’from the given to the impossible’ it requires a

conception of the intended possibility. This is done by generating a conception of how a technology will function and then testing it, with the results feeding back through an iterative learning process leading to modification of the original design” (Nightingale, 1998:698). This is what Layton (1974:696) calls “the purposive adaptation of means to reach a preconceived end”.

The Purposive adaptation of means to reach a ‘preconceived end’ requires a cognitive element that relates to our tacit understanding of how the technology should behave, and an element that describes the intrinsic physical properties of a technology that make it behave in a given way. Technology has no preconceived purpose, it just ‘is’, whereas people have different interpretations and conceptions of the purpose of a given technology, thus the normative function exists only in relation to our tacit understanding of how a technology should behave.

We comprehend things not through ‘single facts of information’ but rather by understanding the interwoven relationships and patterns of behaviour. In practise people are assigning functions to technologies by understanding how it relates to other objects, and it is only possible to understand how things function because we already have a prior understanding of their relation to the world.

Conclusively, “… Technologists must way up the nature of [a] problem in all its interwoven complexity. Often these ‘relative importances’ are either unknown at the outset, unknowable or change as the technology evolves. But they are only comprehendable against a tacit background knowledge. Tacit knowledge is therefore needed to understand a problem, and to comprehend its solution.” (Nightingale, 1998:699)

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8.2.4 The direction argument Together the three building blocks described above explain why scientific knowledge cannot

be directly applied to the innovation process, because science and technological innovation are going in opposite direction – “hence the direction argument” (Nightingale, 1998:699).

At the starting point of the process innovation, the protease department had a rough idea of the end result they wanted to achieve, but the starting conditions required to produce that end result was unknown. Not the other way around. “In essence science is a one-way street and technology is going in the wrong direction” (Nightingale, 1998:700). However, in practise there are alternatives, and this alternative makes up the innovation cycle argument. 8.2.5 The innovation cycle argument The question arising from the three building blocks is: If we cannot extrapolate the desired

end result to find the appropriate starting condition how then do we innovate?

“When designers and engineers imagine a functional solution to a problem, they start with a given background of tacit design knowledge. They cannot just apply scientific knowledge or pattern and expect a result to simply pop out. Instead, the technologists must ‘see’ how the problem they face relates to similar problems they have faced in the past” (Nightingale, 1998:700). This is exactly what is going on in the protease department when developing new enzymes and new screening technologies. The screening process itself is an attempt to test predictions of potential enzymes. They start with the desire to find an enzyme that fulfils a certain customer need. By assuming that similar needs will have similar solutions, they start with an enzyme that fulfil a similar need and run it through the product development process including the screening technologies. In continuation of the example above. To develop an enzyme that removes fat at 30°C instead of 40°C, the protease department assumes that the previous, already known starting condition are optimal to fulfil the customer need and find the enzyme. Thereby, the technologists can take the problem with an unknown solution and compare it to a similar problem with a known solution, and then assume that the solutions will be alike – as illustrated below. Once a solvable problem is in place, it is used to take the initial ‘similar’ solution, and analyse and modify it until it has been ‘tuned in’. This way, over time they can adjust the ‘known starting condition’ to find the exact desired end result.

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Question direction Unknown starting conditions Y

Process Innovation

Known starting conditions Y´

Process Innovation

One way only

One way only

Desired end result X

Similar end result X´

Figur 2-7: The innovation cycle illustration (Nightingale, 1998:700)

The cognitive process in product development is similar to the cognitive process in process development. Whereas the product development process is ‘cyclical’, the process development is more multifaceted and sometimes serendipitous. Nevertheless, the cognitive processes in problem solving are equivalent and compares well to Nightingale’s model (1998). Consequently, we propose the cognitive model of innovation applies to product as well as process innovation. 8.2.6 The theoretical framework in perspective Above we have presented two theoretical frameworks by Boisot and Nightingale respectively.

We will now elaborate on how their cognitive models complement each other and open up to how they are going to be applied on the process development in the protease department.

First of all, the information theory by Boisot (1998) explains the cognitive dynamics that provide the basis for learning and innovation. It explains how codification and abstraction basically work to economise on data processing whereby competitive advantage can be reached through time compression. Moreover, the abstraction process brings clarity to how knowledge is created and translated into new innovative technological concepts. By applying the SLC to the innovation process we will further study the learning process of how knowledge is created. The strength of the information theory is that the SLC explains how new knowledge is created as a result of going through the six phases described above. In terms of Boisot this indicates that knowledge can evolve from a pure ‘brain exercise’ within the head of a single person. However, the I-space theory lacks in clarifying the knowledge 102


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creation process in the social context, as it does not satisfactorily emphasise the importance of tacit and explicit knowledge. For this reason we will supplement our argumentation by use of Nonaka et al. (1994, 1995, 1996). The information theory by Boisot and especially the SLC is closely related with Nanaka & Takeuchi’s (1995) theory on organisational knowledge creation and the four modes of knowledge conversion that supports the SLC. However it is our belief that Nonaka et al. to a better extent manages to explain the knowledge creation process in a social context by emphasising the importance of tacit and explicit knowledge.

Highlighting the importance of tacit knowledge is even more pertinent in relation to the application of knowledge in innovation purposes. By applying the cognitive model of innovation (Nightingale, 1998) we can then explain how such abstract understanding about technology is turned into useful concepts by translating and formulating solutions to new technologies. We do not attempt to integrate the information theory and the cognitive model of innovation. Instead it is our objective to apply them separately and make the most of their complementation.

The SLC consist of first a value creating and second a value exploiting phase. It is our interpretation however, that Boisot’s model primarily explains how the cognitive understanding about phenomenon evolves, and only to a minor extend explains how this understanding is exploited and applied in practise. In support, Nightingale’s cognitive model of innovation explains the role of tacit knowledge in technical change and how scientific knowledge is used in innovation to solve problems and find solutions. Nightingale’s model, so to say, exploits the value created through data processing, codification and abstraction, by combining the tacit background knowledge with the new codified and abstract understanding about product development. Supplementary to Boisot, Nightingale posits that scientific patterns cannot be perfectly extrapolated for complex, non trivial technological development because technical change is dependent on learnt tacit conceptions of similarity that cannot be achieved simply through information processing as suggested by Boisot. That is, Nightingale does not agree that simple information processing will result in a finished end result, however the cognitive process of codification and abstraction work as the mean by which the optimal starting condition is identified, and hence supports the process innovation in its entirety. Naturally, there are cognitive processes that are better explained by Boisot rather than Nightingale and visa versa.

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In total, we posit, the information theory elucidates the ‘value creating process’ whereas the cognitive model of innovation clarifies how such knowledge is applied in innovation, which mean it explains the ‘value exploiting process’. Conclusively, codification and abstraction supports the cognitive model of innovation by building a conceptual understanding of the problem at hand to better identify the most optimal starting condition for innovation, whereby the solution for new technologies are realised. Technologies are constructed socially and embody sociological and political conceptions of problems and appropriate solutions (Nightingale, 1998:689).

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9 ANALYSIS OF THE EMPIRICAL FINDINGS So far in this thesis we have identified the constituent leading to successful competence development in the protease department. Based on our findings and empirical illustration of the constituents of the competence development we will now answer the two subsequent questions. In this chapter we will analyse the process development (the illustration) in the protease discovery department by applying the two cognitive frameworks presented above.

It is our objective in section 9.1 to analyse and answer question 2:

Why have the protease discovery department hitherto succeeded in fostering knowledge leading to competence development?

It is our objective in section 9.2 to answer question 3:

How can the protease discovery department exploit current knowledge

and

competencies

in

future

advanced

process

innovations?

9.1 A cognitive view on process innovation To guide the reader through our analysis we will now explain how the first question is answered. The answer is divided into three main parts.

First of all, we will clarify the concept of the I-space in relation to our case by applying codification and abstraction to the interaction between two people. We will then explain how codification and abstraction work to leverage the overall understanding of the technological system, and consequently how that helps to integrate skills and technologies whereby complementing technologies are developed and incorporated to improve certain areas in the product development process. Essentially for the first part of the analysis, we will apply the Ispace theory to explain how the cognitive process of codification and abstraction economises on data processing and how it helps to reach an understanding of the underlying principles necessary to develop new and efficient screening technologies, i.e. how data from full scale wash can be used to benchmark and develop new screening technologies.

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Secondly, we will look at the process development as a social learning curve in the I-space. This is done to picture the process innovation as a learning process. However, since the SLC does not satisfactorily cover the knowledge creation process we will take a brief detour to emphasize the importance of tacit knowledge in organisational knowledge creation. Nonaka’s framework for organisational knowledge creation emphasises the interaction between tacit and explicit knowledge and its central influence on knowledge creation based on Nonaka et al. (1994, 1995, 1996). More specifically, we wish to elucidate how knowledge creates new knowledge and produce ‘out of the box thinking’. In addition, focus on the characteristics and value of tacit knowledge is important to our subsequent bridge between the I-space and Nightingale’s cognitive model (1998).

Finally, we will bring in the cognitive model of innovation (Nightingale, 1998) to understand how scientific knowledge is translated into innovation and to explain how the innovation process is mobilized by (among others) the abstract and tacit background knowledge in the department. Conclusively, we wish to explain why the department is so successful in developing their screening competence. 9.1.1 Economising on data processing – between two employees The process illustration is in fact a codification and visualisation of the process of building

new and enhancing existing competencies in the protease department. The framework reveals the constituents of the competence and their interdependencies. It shows very concrete constituents, such as key employee skills and key technologies that have played significant roles in the competence development in the department. It visualises how the competence evolved and which other factors have influenced the innovation process.

On the surface, the illustration does not reveal the underlying mechanisms generating competencies. However, by applying the conceptual framework by Boisot (1995) we will now take a closer look at the process development. In this section we take acts of codification and abstraction as holding the key to economising on data processing. Further, we take the securing of such data economies as a crucial prerequisite of effective communication and, by implication, of effective organisational processes whereby competencies are developed.

Before we apply the I-space to the innovation process as a whole, we will first exemplify the theory by focussing on the mechanisms between two employees, namely SFFE and KVJO. 106


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When the department leader in MB SFFE and the department leader in ADU KVJO are coordinating daily work, they are giving structure and coherence to each otherâ&#x20AC;&#x2122;s fields of activities. It provides mutual understanding of the individual processes and raises the level of codification. Theoretically speaking, they are assigning phenomenon to categories. A phenomenon is codified by assigning data of experience to categories to better comprehend the phenomenon going on in the department. Effective codification is one way of economising on data processing and transmission, but if they need a large number of categories to apprehend a phenomenon, they may not be much advanced. By reducing the number of categories that they require to deal with phenomenon, abstraction then becomes the means through which they economise on data-processing and transmission efforts (Boisot & Griffiths, 1999:666). They can achieve mutual understanding of each others work through a process of abstract conceptualisation, based on their understanding of both the phenomenon itself as well as what they want to do with it.

For example, coordination between SFFE and KVJO gives structure and coherence about particular screening processes in MB and ADU. Because they posses a high level of integrative knowledge they perform the ability to codify and combine specific knowledge domains about particular screening processes. Gradually they achieve an understanding of both what data is relevant to themselves as well as what data is relevant to the other part. This way abstraction, by giving some structure to the phenomenon, channels efforts at codification into relevant areas, whereby SFFE and KVJO use fewer resources to process and coordinate the same amount of data. The codification of one screening process thus reduces uncertainty initially associated with the cooperation between SFFE and KVJO leading to an increased level of abstraction. Thereby they can each generalise the application of newly codified insights to a wider range of situations, e.g. another similar screening process in a subsequent product development. This involves reducing processes to their most essential features from specific knowledge to abstract knowledge about technical artefacts and processes within each field of activity, i.e. conceptualising them. The codification and abstraction of the processes work in tandem and as the picture materialises in codified form it allows for diffusion.

Conclusively, when two people perform the ability to economise on data processing they reduce the time and resources spent on comprehending a given situation and they optimise the

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speed at which they can solve problems. Ultimately, this is the path to competitive advantage and may be the key to competence development in the protease department. 9.1.2 The development of complementing technologies Above we have focussed on the processes between a few people. However over time, looking

at process development as a whole, codification, abstraction and diffusion works to improve the overall understanding of diverse processes in the department. As a result of implementing AMSA, several complementing technologies needed to be developed to solve bottle neck problems in the product development process. As a result of closer cooperation, job rotation and deliberate efforts to share knowledge, a codified and abstract understanding of various complementing technologies diffused in the department. More people got involved in the development of AMSA and the level of codification and abstraction enhanced. It gradually became obvious how to solve problems with complementing technologies. This included in particular micro- and large scale purification, the AST method, mini wash and the automation technology. The overall cognitive understanding of the technologies improved. The higher level of abstraction worked to reduce uncertainty about cause and effect relationships. In other words, as the number of data categories needed to deal with the screening process is structured and classified, they can be evaluated, prioritised and eventually reduced to represent the essential cause and effect relationships. An abstract understanding of the screening principle then helps to comprehend the interrelation between technologies and identify areas for further improvement.

For example, the number of categories needed to draw on to comprehend mini wash is minimised, by what means data is processed and transferred more efficiently. As the right data is channelled to the right people, coordination and integration optimises. This is visualised in the illustration where increased fermentation capacity, large scale purification and the new AST method integrate to increase the capacity of mini wash. 9.1.3 The relation between AMSA and test data from full scale The process development is an attempt to continuously improve the screening technologies in

the protease department. As previously mentioned, the purpose of setting up the full scale wash in China was primarily to reduce cost in full scale testing. However improved skill and statistical software developed to extract useful data redefined the importance of the test lab. The data could now be used to benchmark the screening technologies in MB and ADU. Thus,

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the quality of a screening technology is partly determined by its ability to select enzymes that perform as intended when tested in full scale wash.

In phase II in the process illustration experience from the full scale lab in China somewhat codified. Skilled programmers improved statistical data-processing software significantly to better extract and structure data from full scale wash. Moreover, co-location of test labs with several customers increased the reliability of data from China to correlate with customerâ&#x20AC;&#x2122;s test results. Theoretically speaking, codification works to assign test data to categories and abstraction to reduce the number of categories needed to extract useful information from test results.

By codifying and analysing test data, the protease department can extract how different enzymes function and react in full scale wash. Further, by understanding the essential principles behind full scale wash they can examine how to improve or develop a screening technology that replicates the full scale process into a high through-put screening technology. Particularly during phase II in the process development, more or less unconsciously, codification and abstraction worked to structure test data, reduce categories of cause-andeffect relationships, and extract data that are redefined and translated into such new screening principles. This way the protease department unconsciously economise on data processing from full scale and more efficiently develop new screening technologies. Conclusively, the Ispace theory can be exerted firstly as a cognitive process to economise on data processing, and secondly as a process to reach an understanding of the underlying principles necessary to develop new and efficient screening technologies. Currently this is not consciously put into practice in the protease department. Foremost, the focus on screening has been to replicate the processes in full scale and to develop a miniature technology AMSA to match end-user condition (application) and to reach a high through-put screening method.

Figure 2-8XXXX summarises and illustrates the process of replicating the full scale process into an effective screening technology. As visualised, enzymes from AMSA are correlated with full scale wash, and enzymes from full scale are correlated with tests by customers. The closer to end user condition the AMSA technology screens, the more efficient the screening process.

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3) Translate principles

4) Define or Redefine...

2) Abstration

Replication process

Enzymes AMSA

Correlation

1) Codification

Full scale

Enzyme

Customer enzyme

Correlaion

Figure 2-8: Cognitive process to replicate a miniature screening technology. As a preliminary conclusion, the reason why the protease department manages to develop the screening competence can be assigned their ability to achieve an abstract understanding of the complex system of technologies whereby the can economise on data processing, i.e. they enhance their ability to comprehend problems and find solutions faster and more efficiently. The overall technological clarification enables the department to solve bottle neck problems and improve complementing technologies. Moreover, test data from full scale is used to benchmark on screening technologies in the product development process.

Codification and abstraction foster the learning process to continuously improve the screening competence. We will now examine how this learning process, the social learning curve, relates to the process illustration. 9.1.4 The Social Learning Curve (SLC) The process innovation from 1999 to 2004 is of course a result of an intense learning process.

To shed new light on the learning process it will now be analysed from a social learning curve perspective (Boisot, 1998). As previously emphasized, the SLC can be considered both a long term and short term advancement, i.e. it can take place over weeks or years depending on the situation in focus. Moreover, the SLC continuously takes place between individuals or different groups and as a consequence new knowledge develops. The sequencing suggested by the SLC should be thought of as schematic. On a micro scale many of the steps run concurrently so that what we are in effect dealing with are the broad resultants of data flows

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in the I-space. Codification and abstraction, for example, may run almost together if those responsible for structuring new knowledge are also responsible for applying it in different areas. The same goes for abstraction and impacting. This section primarily serves to exemplify how the SLC applies to the overall process innovation from 1999 to 2004. We will return to the evolution of different types of SLCs in section 9.2.

Likewise applying the I-space to concrete practices between e.g. SFFE and KVJO occurring within days or weeks, we could consider the whole process innovation from 1999 to 2004 as one SLC running over 5 years. The I-space thus applies to both the individual as well as an organisational level which is useful to obtaining a more abstract understanding of the illustration. To avoid making things more complicated and to avoid repeating our selves, we will not go into detail with how the I-space applies to the process innovation. Instead by following step 1 â&#x20AC;&#x201C; 6 in the SLC we can explain the overall correspondence between the SLC and the process innovation. Please follow the subsequent analysis in correspondence with the process illustration in appendix.

2004

1999 1. 2.

Scanning Problem solving

Phase I

3. 4.

Abstraction Diffusion

Phase II

5. 6.

Absorption Impacting

Phase III

Figur 2-9: The relation between the SLC and the process innovation Phase I consists of scanning and problem solving. As described in activity one in chapter 7 the first phase of the process visualises how MB and ADU solve the basic screening problem by means of scanning the market for opportunities and threats. Pressure from competitors, inputs from customers and cooperation with external partners gave rise to AMSA 1. The protease department realised the need to develop a new screening technology that better corresponded with end-user conditions. As clarified, the efficiency of the screening technology depends on the correlation between test results from the screening technology and test data from full scale wash which is equal to end-user conditions. Thus the objective in

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phase I is to understand the end-user processes under which the enzyme must function and codify the principles under which such processes must operate.

From a theoretical perspective, problem solving initiated in the uncodified region of the Ispace is often both risky and conflict-laden and, practically speaking, at this point it was uncertain whether AMSA was the right technology to solve the problem. However, hard work between SFFE and Proinvent as well as support from top management pushed AMSA 1 onto phase II as visualised in the process development illustration.

Phase II can be interpreted as an abstraction and diffusion of knowledge. Basically knowledge about AMSA 1 codified and the uncertainty initially associated with the technology was somewhat eliminated. The codification led to an overall improved conceptual understanding of the screening technologies in both departments and various employees got more associated with their essential features. Meanwhile, coordination between the departments and interdepartmental job rotation meant that both departments got increasingly integrated whereby the gradually more abstract understanding of the screening technologies could be shared and diffused.

In other words, as the full scale lab in China got properly implemented and its supporting technologies matured, more useful statistical data could be extracted to feed back into the screening processes in MB and ADU. Phase II was important because newly codified insights were applied to a wider range of situations. E.g. knowledge about AMSA 1 conceptualised and it became clear what other supporting technologies were necessary to solve bottlenecks around AMSA 1. It became clear in the department as a whole, that the key to successful product development resided in the correlation between full scale data and screening test results. I.e. a profound understanding and replication of full scale washing principles into micro scale screening principles.

Phase III is where the accumulation of learning converges and the codified and abstract insights are applied to different situations in a ‘learning by doing’ or a ‘learning by using’ fashion. Absorption and impacting also work in tandem, and after four years of intensive work the embedding of abstract knowledge into concrete technologies materialised into AMSA 2b and AMSA 3b to be fully implemented and automated in each department. As

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previously mentioned, the application will now spin off new questions. However, over time, such new insights develop a penumbra of uncodified and specific knowledge, personal intuitions that reflect the history of past experience which helps the protease department to guide their application of knowledge under new circumstances in the future. This is what Nightingale (1998) describes as the accumulation of tacit background knowledge, which is important for the translation of scientific knowledge into innovation purposes. We will return to this in the subsequent section.

In total, when we look at the process development as one SLC it makes sense to state that the cumulative learning experience is embedded into AMSA 2b and 3b. Likewise, depending on the time perspective, we could argue that three sequential SLCs generate learning that is embedded into AMSA 1 (phase I) AMSA 2 (phase II) and AMSA 3 (phase III) respectively.

Conclusively, we can think of the process innovation as a SLC, where codification and abstraction is driving and accelerating learning mechanisms through a more efficient communication process. The learning process drives the continuous innovation of improved technological solutions in terms of new and optimised technologies supported by updated complementing technologies. For now we will merely keep the SLC in mind and return to a deeper interpretation in section 9.2. 9.1.5 A note on organisational knowledge creation Essentially, the I-space reveals, that it is the interplay between codification and abstraction

that gradually transpires and transforms the understanding of technological concepts which partly determines the key to competence development. Boisot and Griffiths (1999:665) states that “… knowledge can be thought of as dispositional4: It is those prior beliefs on which we are willing to act. It consists of a set of probability distributions which orient our behaviour and which in turn are modified by incoming information extracted from data. In sum, data is ‘out there’, and we make what we believe to be information selections from it to modify what resides ‘in here’ in the form of tacit knowledge’.

4

Popper, K. R., (1993) Realism and the Aim of Science, London: Hutchinson.

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The I-space only vaguely involves the tacit dimension of how “… structured data that we gradually internalize through practise” (ibid) turns into knowledge. Therefore, we consider the I-space as ‘in the box thinking’ whereas we believe we need to ‘think out of the box’, i.e. how to create knowledge that will lift us out of status quo. To do so, we will now take a brief look at one of Boisot’s sources of inspiration – Nonaka & Takeuchi (1995).

Drucker (1993) argued that knowledge is “… the only meaningful resource” in business today. However, despite all the attention devoted by the leading observers, only few of them have examined how business organisations create knowledge. With this section we do not wish to explain in detail the creation of organisational knowledge, rather we wish to emphasize the interaction between tacit and explicit knowledge and its central influence on knowledge creation based on Nonaka et al. (1994, 1995, 1996). More specifically, how knowledge creates new knowledge and produce ‘out of the box thinking’. In addition, focus on the characteristics and value of tacit knowledge is important to our subsequent bridge between the I-space and Nightingale’s cognitive model (1998).

Boisot argues that learning takes place either when new agents enter the I-space or through the cyclical movement of existing knowledge in the space in terms of the SLC. Thus, learning is supposed to evolve from the codification and abstraction process, either in a social context or within the head of a single person. Along the lines, by stating that new knowledge is created through internalisation Boisot (1995) is referring to Nonaka & Takeuchi’s (1995) four modes

of

knowledge

conversion:

socialisation,

externalisation,

combination

and

internalisation. Internalisation is a process of embodying explicit knowledge into tacit knowledge. It is closely related to ‘learning by doing’. When experiences throughout socialisation, externalisation, and combination are internalised in individuals’ tacit knowledge bases in the form of shared mental models or technical know-how, they become valuable assets (Nonaka et al., 1996:840). Compared to Boisot (1995) this is when “…codified and abstract data develops a penumbra of uncodified and concrete insights, personal intuitions that reflect the experiences from the past”. This process is likely to develop the background knowledge that according to Nightingale (1998) is imperative for translating the abstract conceptions of technology into new innovative screening principles.

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Socialisation aims at sharing tacit knowledge. On its own, however, it is a limited form of knowledge creation. Unless shared knowledge becomes explicit, it cannot be easily leveraged by the organisation as a whole. Also, a mere combination of discrete pieces of explicit information into a new whole does not really extend the organisation’s existing knowledge base. But when tacit and explicit knowledge interacts, an innovation emerges. “Organisational knowledge creation is a continuous and dynamic interaction between tacit and explicit knowledge” (Nonaka et al., 1996:842).

As oppose to the I-space, as noted above, Nonaka et al. (1996:842) emphasise that an organisation cannot create knowledge by itself. Individual’s tacit knowledge is the basis of organisational knowledge creation. Hence, tacit knowledge is the key to innovation. The protease department is mobilising tacit knowledge created and accumulated at the individual level, in terms of specific, integrative and deployment knowledge. The mobilised tacit knowledge is then organisationally amplified through the four modes of knowledge conversion and crystallised at higher ontological levels. Nonaka (1994) call this the ‘knowledge spiral’.

Among the four modes of knowledge conversion, externalisation holds the key to knowledge creation, because it creates new, explicit concepts from tacit knowledge. Those explicit concepts are crucial to innovative thinking, and to our interpretation, this is what drives the process development in the protease department. But, how can we convert tacit knowledge into explicit knowledge effectively and efficiently, and apply such knowledge to develop new screening technologies? Before we turn to Nightingale to explain how scientific knowledge is translated into innovation purposes, we will first incorporate Nonaka’s view on the role of tacit knowledge in knowledge creation, and, to all intents and purposes, how such tacit knowledge is made practical in innovation.

Nonaka & Takeuchi argue that particularly externalisation is the knowledge conversion process at which inventiveness is brought about. The answer lies in the sequential use of metaphor, analogy, and model (Nonaka & Takeuchi, 1996:838) that are generated during conversation and applied in innovation. Most of what Michael Polanyi called ‘tacit knowledge’ is expressible in so far it is expressible in terms of metaphor. Metaphor is a way of perceiving or intuitively understanding one thing by imagining another thing symbolically

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(Ibid). Thus, metaphors are one communication mechanism that can function to reconcile discrepancies in meaning, and how we comprehend phenomenon.

Because metaphor is ‘two thoughts of different things … supported by a single word, or phrase, whose meaning is a resultant of their interaction’ (Richards, I.A., 1936 in Nonaka & Takeuchi, 1996:839), we can continuously relate concepts far apart in our mind, even abstract concepts with concrete ones. The creative process continues as we think of their similarities and feel an imbalance, inconsistency, or contradiction in their association, thus often leading to the discovery of new meaning or even to the formation of new paradigms. This cognitive process is put into practise when for example engineers in the protease department are using their ‘tacit background knowledge’ (Nightingale, 1998) to convert the functionality of one technology to application in another function. For example, when GEAB translates his ‘automation knowledge’ (see process illustration) and convert the functionality of the pipette robot from micro purification to the handling of AMSA assays, he might formulate the innovation through metaphor, imagine their similar functionality and model the technology in practise. Contradictions between two functionalities are then harmonized by analogy, which reduces the unknown by highlighting the commonness of two different things. Thus analogy helps us to understand the unknown through the known and bridges the gap between an image and a logical model (Nonake & Takeuchi, 1996:839).

Nonaka’s view on the application of tacit knowledge in organisational knowledge creation corresponds well with its application in the cognitive model of innovation (Nightingale, 1998). We will now discuss how such knowledge is translated into innovation processes in the protease department. 9.1.6

Applying knowledge in process innovation

“… science cannot be directly applied to produce technology because science answers the wrong question. Innovation starts with a desired end result and attempts to find the unknown starting conditions that will achieve it. Scientific knowledge, by contrast, goes in the opposite direction, from known starting conditions to unknown results”. (Nightingale, 1998:689)

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Nightingaleâ&#x20AC;&#x2122;s key statement very well describes the dilemma in Novozymes. We are aware that the cognitive model of innovation is based on product development in particularly the pharmaceutical industry, and we agree that the product development process, of finding the exact enzyme to solve a particular end-user problem, is a situation caught in a desired endresult while the starting condition is unknown, - at least to a certain extent! However, it is also our interpretation that process development in the protease department is a similar quandary, and hence, we believe that the cognitive model of innovation applies well to the analysis of the process development from 1999 to 2004.

Accordingly, we posit that the process development from 1999 to 2004 is a situation of a desired end-result in terms of a new and better screening technology, whereas the starting condition to develop such screening technology is unknown. They know they need to boost screening through-put meanwhile screening under conditions as close to the end-users (full scale) to reach an applicable solution. The desired end-result in 2004 is somewhat determined but the trajectory of how to arrive at the new product development process is unknown. In other words, they are aware that they need to enhance the product development process, but they are uncertain how to accomplish such complex process innovation and thereby arrive at the desired end result. Thatâ&#x20AC;&#x2122;s the point of departure in the year 1999 where justifiably the starting condition is unknown.

Only now in retrospect we can clearly explain and visualise how this process development was accomplished. The framework visualises the generative skills and technologies, and the coordination-, integration- and learning mechanisms that altogether enhanced the product development process between 1999 and 2004. However, the process of arriving at the desired end-result cannot simply be codified and explained in tangible idioms. Instead we will now abstract from our current perception of the process illustration and extract the underlying methodological approach to how Novozymes is solving problems and thereby developing new competencies. 9.1.7 Developing AMSA 1 â&#x20AC;&#x201C; a case example To provide the reader with an in depth understanding of how the cognitive model of

innovation applies to our case, we will start out by explaining the development of AMSA 1.

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It is important to understand the complexity of the starting condition. As highlighted in the case description the product development process in 1999 was different in several aspects. The central problem was however that no screening technology existed in between FITC and mini wash, and so 500 proteases where randomly selected out of 100.000 potential enzymes. Because of this, the selection process was ineffective and partly based on luck rather than systematic decision making. In 1999, the main screening technology in the product development process replicating end-user condition was miniwash, and hence the only reference for further innovation. It was necessary to invent a whole new screening technology to fill in the gab of serendipity. Obviously the starting condition was unknown.

As previously mentioned MAS was the central character and the instigator of AMSA 1. He possessed the necessary background knowledge about different technologies and their interdependencies. The input from a customer triggered the original idea to develop a new screening technology. By assuming that the technology at the customer site could solve a similar problem in the protease department, the engineers could take the problem with an unknown solution and compare it to a similar problem with a known solution, and then assume that the solutions would be alike. Once the prototype was in place it could be analysed and modified until it was ‘tuned in’ to AMSA 1. The example may seem simple, but in fact trying to explain how the idea materialised was very difficult for the workshop participants. It requires a much deeper understanding than they or we can express in tangible idioms. The mechanisms that cannot be explained are intangible and impossible to codify. We cannot explain the exact cognitive process going on in MAS’s head when he first realised the significance of the new screening technology. MAS was introduced to the technology during a customer meeting. His tacit background knowledge about micro biology and screening technology then gave him the capacity to interpret information and comprehend things that cannot be codified (Nightingale, 1998:693). “Thus, tacit knowledge is both the background of interwoven experience and the automatic capacity we have to relate experience to it. It is hard (if not impossible) to codify and transmit because it is the background to which codified transmitted information is compared” (Ibid). 9.1.8 The cognitive process in successful innovation Theoretically speaking the cognitive process described above requires three things, 1) a tacit

background knowledge about the product development process and the capacity to interpret information and comprehend things that cannot be codified, 2) the ability to form an abstract 118


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correspondence between three levels of patterns, which provide the capacity to explore patterns on one level and apply those patterns on another level, 3) it requires a conception of the intended possibility. This is done by generating a conception of how a technology will function and then testing it, with the results feeding back through an iterative learning process leading to modification of the original design. Thus, scientific knowledge (of pattern) is not applied directly to produce technology, but indirectly to help test uncertain functional solutions, which have been produced by following technological traditions (Nightingale, 1998:703). Analysis and testing allows engineers to understand how changing the starting conditions affect the end result. This knowledge can then be built up and extrapolated to fine tune the technology to produce its intended behaviour.

In combination, the three theoretical building blocks summarise the cognitive process of translating an unknown starting condition to a known, and essentially it explains how knowledge (codified or abstract) is applied to innovation. The cognitive model describes the process in which knowledge is applied to innovation in the protease department. The primary task arising is then to reach the optimal starting condition through in-depth comprehension of the situation at hand.

The process innovation is intrinsically uncertain because uncertain patterns of behaviour are being extrapolated into the unknown. From ‘post it!’ notes to oil platforms, engineers can only know ‘for sure’ about failure after the technology is developed. They must therefore rely on getting as good an understanding as possible to optimise the starting condition for the innovation process. The better this starting condition is composed, the closer the innovation process arrives at the desired end result. It is a process of answering the question ‘what causes the end result’ and therefore a mixture of recognising the problem as similar to a previous situation, and experimenting to establish that similarity. The result of which resolves the problem to a more specific level. Achieving the necessary understanding to optimise the starting condition depends on the level of codification and abstraction, and the speed at which the starting condition is resolved depends on the ability to economise on data processing. Despite we see the information theory (Boisot, 1995, 1998) and the cognitive model of innovation (Nightingale, 1998) as distinct cognitive processes, we wish to emphasise that important interrelation and interdependency between the two.

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Abstraction enables engineers to reduce complexity to identify the decisive cause and effect relationship. Tacit knowledge enables engineers us to ‘see as’ rather than simply ‘see’, because they can actively interpret experiences from previous processes. Their background of experiences stores as tacit knowledge that acts to fill in missing parts of understanding in new situations. Thus background knowledge helps to solve incomplete patterns by relating previous experience to new situations and hypothesise the best solution to a given problem. In combination with an abstract understanding of complex technologies, the engineers compose better hypothesises and reach better solutions.

Engineers developing AMSA comprehend things not through ‘single facts of information’ but rather by understanding the interwoven relationships and patterns of behaviour. In practise they are assigning functions to technologies by understanding how it relates to other objects in the department, and it is only possible to understand how things function because they already have a prior understanding of their relation to the system of technologies in both MB and ADU.

Similar to Boisot’s ‘abstraction’, Nightingale (1998:695) argues, that the ability to perceive patterns reduces the amount of information needed to understand a given system of technologies and hints at an underlying order that can be abstracted by recognising patterns. Nightingale is, likewise Boisot, pointing at the ability to economise on data processing. Transferred to the cognitive model of innovation, data processing brings about the ability to find the optimal starting condition, - faster and more efficiently. As documented in the theoretical presentation above, Barrow (1998, 1991) describes science using the metaphor of ‘information compression’. We believe this ability, to perceive the best starting condition, plays a significant role in the competence development. Novozymes, so to say, has reached a time compression competitive advantage that is very difficult for any other competitor to imitate. It is impossible to copy and undergo the same leaning process, and when left behind, even more difficult to catch up. In this case the cognitive processes work to establish the imitation blockage. Consequently, we posit, it is the value creating mechanisms, and not the value retention mechanisms, that lead to competitive advantage. 9.1.9 The cognitive process in conclusion To conclude on the first part of the analysis and thereby answering research question 2 we will now discuss why the protease department has succeeded in fostering knowledge leading to

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competences development. We will discuss the findings from a practical view point and how the competitive advantage is reflected in practical activities. We will relate the advantage in process innovation to the direct and indirect competitive advantage in product development which to the observer may seem to be the most evident competitive advantage.

9.1.9.1 Process innovation One thing is theory â&#x20AC;&#x201C; another is practise! The theoretical discussion may seem somewhat superficial and vague on observable practises. It is our intension with this concluding section, to portray the theoretical findings into more useful and practical terms.

As clarified in the previous chapters, human skills, technologies and organisational processes constitute the main constituents building new competences. Moreover, as presented in the sections above, several cognitive aspects are leading to successful competence development in the protease department. Codification and abstraction are the cognitive processes through which individuals and groups, or so to speak, the department as a whole manages to comprehend the technological system, and through which they come to economise on data processing. But more exactly, how is that translated into successful innovation and how does that become a competitive advantage?

In relation to the process innovation, economising on data processing basically means reducing the time necessary to comprehend a given technology or system of technologies. This way employees use less resources to comprehend the interwoven technological complexity. This clarity of technological interrelationships provides the basis for faster decision-making and more efficient problem solving. To Novozymes, the result is a â&#x20AC;&#x2DC;time compression advantageâ&#x20AC;&#x2122; that is difficult to circumvent by competitors, and it would be very difficult for another company to imitate and experience the same or similar learning process. The learning cycle of codification and abstraction is simply necessary to reach the same level of abstract understanding of the technological system. As we argued in section 7.3, even though the technologies are somewhat generic and could be obtained on the market, it is the tacit background knowledge developed over time that eventually enables the engineers to translate abstract knowledge into innovation purposes. Finally, from a cognitive perspective it seems fair to conclude that the core competence in terms of the time compression advantage developed between 1999 and 2004 is inimitable and possibly sustainable.

As discussed in the analysis, this learning experience and the level of abstraction further provides the basis for translating knowledge into innovative solutions. The learning experience and

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background knowledge provides engineers with the ability to extrapolate desired future endresults back to identify better starting conditions for innovation. This way the cognitive process on one hand work as value creating and on the other hand as value exploiting, whereby knowledge is translated into new competences, in terms of developing new and better screening technologies â&#x20AC;&#x153;â&#x20AC;Śto screen under as realistic conditions as possible while maintaining high through-put and high quality assaysâ&#x20AC;?.

The initiative to set up full scale wash in China proved to be a valuable choice. Without full scale test lab in china, the department would not be able to verify the screening results from AMSA on a large scale and in close relation to the application purpose. Because the full scale lab tests the potential enzyme in a large number of tests rather than a few examples, the test is much more efficient. This way, they can confirm the quality and functionality of the enzyme before it is applied at end-user condition. This process has vastly improved the information flow, and the statistical programmes have enabled the information efficiency, feeding back into the process innovation. This has resulted in the opportunity to benchmark screening technologies against full scale and to translate data from full scale wash, whereby screening technologies and the entire screening process has been optimised.

9.1.9.2 Product innovation To the outsider, process innovation probably seems to be an indirect competitive advantage while the end-user product is perceived to be the actual result of superior competencies in the department. Hence, we find it relevant to briefly comment on the direct competitive advantage seen from the outside of the company.

As we concluded in chapter 7, the perception of successful screening is to find a balance between high through-put, reproducibility and application. High through-put is a matter of the screening capacity, reproducibility determines the number of screens needed to achieve reliable test results, application means screening under conditions as close to end-users as possible to reach a close match between test results and end-user applicability. Naturally, high through-put is important to speed up the screening process so as to test as many potential enzymes as possible. Reproducibility is necessary to reduce the time spent on each screening cycle while still achieving reliable test results. Application is important because currently full scale screening principles are the most optimal screening parameters producing reliable test results. Therefore full scale wash is replicated into multiple miniature washing machines (AMSA) to match the end-user washing conditions. This has been achieved by codifying the screening principles and test data from full

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scale wash and then correlating those results with screening results from AMSA. This way the AMSA technology has been fine tuned during the past 4 to 5 years. Today AMSA has moved a considerable amount of screening principles back through the product development process to test under such principles at an earlier stage in the product innovation cycle. Consequently, the screening process has become highly efficient and the product innovation cycle has as mentioned been reduced from 10 to 5 month.

Consequently, the result of the continuous process innovation is a reduction of the product development process from 10 to 5 month on average. To the outsider, e.g. Novozymes’ customers, this may very well be perceived the direct competitive advantage, while the ability to process innovate is perceived to be an indirect factor. To us, it is an important discussion whether the root to competitive advantage lies in the process or product development, but possibly it founds on both. The answer remains the observer’s; however it is important to notice that the time compression advantage transpires through the entire organisation whether it is a process or product innovation focus.

9.2

Optimising the starting condition in process innovation

In this part of the analysis, we wish to answer the second research question of how the protease discovery department can exploit current knowledge and competences in future advanced process innovation.

In the sections ahead, we will take a closer look at the protease department to reconsider whether the process innovation occurs at a codified or abstract level. We will return to the Ispace theory and present a revised interpretation of how the process innovation transpires in relation to the SLC. Here we distinguish between codified and abstract levels of SLCs. The intriguing question is how increased abstraction and simple translation of technological principles can be applied in future process innovation. By doing so, we will reveal how the process innovation currently evolves within a ‘codified space’ whereas the future opportunities may reside in a more ‘abstract innovation space’. Further we will discuss how an abstract innovation space helps to identify the optimal starting conditions for process innovation even more precisely. Obviously, the better starting condition the more cost efficient and effective the process innovation turns out.

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The reader should keep in mind that our interpretation is from a pure theoretical viewpoint that may not practically be realisticâ&#x20AC;Ś 9.2.1 The interpretation of the screening competence First, we will reconsider how the screening competence in the protease department can be

perceived. As we concluded in section 9.1 the perception of successful screening is to find a balance between high through-put, reproducibility and application. This is done by codifying and replicating full scale wash into the high through-put miniature screening technology AMSA, whereby reproducibility and application is optimised. So far so good, but now we could question whether this is the most optimal screening technique or whether there are other more sophisticated ways to define the screening parameters.

The AMSA technology is in fact testing on multiple categories in order to replicate full scale wash. The process is very complicated and reducing full scale wash to AMSA, i.e. two drops of water, - impedes the chance of covering all relevant variables sufficiently. In other words, the more categories the screening technology must cover, the more problematic it is to actually screen on those categories appropriately. Theoretically speaking, by reducing the number of variables uncertainty can be reduced. However, this requires the ability to identify the essential cause and effect relationship between screening principles and the decisive screening categories. Instead of replicating the full scale wash into miniature format, the protease department must abstract from their current perception of the screening technique to deduct new screening parameters.

Finding the essential cause and effect relationship requires a very high level of abstraction. We will now return to the SLC to reinterpret the learning cycle from 1999 to 2004. 9.2.2 The barrier to abstraction The SLC can be considered an interpretation and visualisation of the broad resultants of data

flows in learning over time. As a result, the SLC can take many different shapes depending on the situation. For example, some SLC may indicate a blockage towards diffusion because the company keeps internal processes secret and consequently confine the operations to a small percentage of those who participate. Other SLCs may have barriers towards scanning because the company operates too self sufficient or because scanning conflicts with industry norms or legal issues.

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The AMSA technology is as highlighted a miniature replication of full scale wash that seeks to find the optimal balance between high through-put, reproducibility and application. This is achieved by codifying and replicating the biochemical processes under which the enzyme must function (full scale wash) and by codifying and replicating the principles under which such processes must operate, i.e. the new screening technology. In practise it means codifying the technical and biochemical principles and then replicating them into a miniature technology attempting to cover some of the same test variables. Thus, it is our interpretation that the current innovative process is performed at a codified level, which in terms of Boisot (1998) is not the optimal learning condition. Due to the characteristic of the replication process, we believe the protease department is facing what we will denote a barrier to abstraction. The barrier to abstraction is visualised in the figure below by illustrating a SLC that only reaches a low level of abstraction, i.e. the curve does not circulate to the back of the I-space.

Codified

Uncodified Abstract

Diffused Concrete

Undiffused

Figur 2-10: The SLC with a barrier to abstraction. Due to this barrier, the department is not fully economising on data processing. Moreover, because they innovate at a low level of abstraction, they do not fully comprehend the data from full scale wash to be perfectly exploited in the innovation of screening technologies. When such comprehension is incomplete5, the basis for innovation is equally incomplete and as a result the optimal technological solution is hardly found.

5

In practise it is unlikely that any innovation process will ever reach an ultimate abstract level as there is always room for improvement and interpretation, the environment is drifting, and because technology is equivoque (Weick, 1990).

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9.2.3 Extracting proxy data for process innovation Our evaluation tells us that the department can benefit from an increased level of abstraction,

through which they will not only economise on data processing but more importantly, they will be able to identify a more favourable starting condition for further innovation. We will now explain and exemplify how an abstract level of innovation will benefit the process development.

The development and efficiency of AMSA is benchmarked against data from full scale. For example, if the proteases suggested by AMSA correspond with test data from mini wash and full scale, it is likely that those proteases will function at end-user level. Contrary, if the test data do not match, there may be found a need to improve the AMSA technology or to change screening parameters. Since full scale is the ultimate screening technology to test how the proteases react in real life, obviously it is useful to analyse test data from full scale to understand the principles determining successful vs. unsuccessful screening processes. Consequently, by codifying data from full scale the protease department come closer to categorise the cause and effect relationship between AMSA and useful proteases. However, a large amount of more or less decisive categories only confuses the picture. For this reason they need to simplify the picture by reducing the number of categories to identify the central cause and effect relationship. By reaching a more codified and abstract understanding of the screening principles, this is where abstraction works to reduce the categories of cause and effect relationships and whereby they can extract proxy data that determine the essential screening parameters. If it is possible to extract the essential proxy data, the screening technology can be significantly simplified and uncertainty of testing on a large number of categories in the screening process can be reduced considerably.

We should mention that this is still a hypothetical proposal; however, the idea was positively received by the protease department. 5 years ago it also seemed unrealistic to propose the idea of developing AMSA, nevertheless today the technology is a reality, - and innovation doesnâ&#x20AC;&#x2122;t stop here!

This suggests that they should move away from replicating full scale in miniature form in terms of AMSA. Instead they must abstract from the current technique and extract proxy data that can determine a new set of parameters for efficient screening.

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Let us again think of an end-user need to remove fat stains at 30°C instead of 40°C. Since they already have a protease that works at 40°C they already have a good sense of the starting condition. Let’s anticipate that the essential parameter to remove fat stains at 30°C can determined by one single cause and effect relationship. If for example the department has reached an abstract understanding of the relationship between fat stains and enzymes knowing that within a certain category of previously identified enzymes removing fat stains at 40°C, they can determine the exact new enzyme removing fat stains at 30°C by exposing the potential enzymes to e.g. a certain chemical fluid or a certain temperature. By finding the essential cause and effect relationship, they then hold the foundation to develop a new screening technology that can test on that single cause and effect relationship instead of a large number of parameters.

Even with this fundamental understanding of cause and effect relationships in place, the problem is still how to accomplish the innovation of the screening technology. As discussed previously, achieving an abstract understanding of the principles defining the screening parameters is not equal to having a conceptual understanding of how the screening technology should be engineered. The principles must be translated into innovation in terms of Nightingale’s model of innovation. We will return to this issue below.

However, if such screening technology is developed, they can then screen e.g. 10.000 potential enzymes and end up with 5 highly qualified proteases, because the central cause and effect relationship is already found. Consequently, intermediate screening processes would be rendered obsolete. This would be similar to the affect of implementing AMSA in 2000, when FITC was rendered obsolete, and the screening process improved radically from 1999 to 2004. The final step is then to test the proteases in full scale wash to determine the most practical and stabile product. This way the screening technology becomes much more efficient and the innovation process is reduced significantly. Conclusively, by obtaining an ‘abstract innovation space’6 the department would not only economise on data processing, but more importantly, they would be able to deduct single 6

Boisot (1998) does not exactly use the phrase ‘abstract innovation space’, however we believe it corresponds well with a SLC that fully exploits codification and abstraction in the learning process.

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cause and effect relationships from test data in full scale and other screening technologies, whereby an overall complete comprehension can be achieved. This may sound unrealistic; nevertheless actions are already taken, to accomplish this goal in the near future.

An example of how this advancement is currently being realised in the department and in Novozymes as a whole, is in the data accumulation into enzyme libraries. As visualised in the product development process in chapter 7.3, product development progresses from 1 mill potential enzymes to end up with one final enzyme solving customer needs. As experience builds up in the department a diversified enzyme library accumulates knowledge about the relation between enzymatic structure and customer application. Scientifically simplified they build up knowledge about where to start searching next time they want to develop an enzyme with similar characteristics or for a similar customer need (application). When a chemical engineer has a tacit feeling of why certain chemical structures work better than others. This is what Nightingale (1998:704) refers to as â&#x20AC;&#x153;chemical intuitionâ&#x20AC;?. Over time, the enzyme library builds up to incorporate more and more enzymes and the cause and effect relationship between enzymatic structure and functionality, i.e. customer application. This process compares to the current mapping of human genes, where for example the exact relation between the genetic structure in humans and a particular disease is identified and mapped. This ability might in the future enable the protease department to pick enzymes out of the library, and more or less instantly produce and deliver the product to the customer. 9.2.4 The abstract innovation space The example mentioned above is in the future and beyond the scope of this thesis. For now it

is necessary to focus on developing screening technologies that can extrapolate the desired end-user need and identify the associated enzyme. In this section we wish to summarise and conclude on how the protease discovery department can exploit current knowledge and competencies in future advanced process innovations?

By extracting proxy data the engineers come closer to identify the technological principles determining the design parameters of a new screening technology, i.e. they can better identify the desired end-result of how a new screening technology must function in practise. Because knowledge about the technology is not being used to produce answers, but rather to produce understanding about how technology work, or more often do not work, this understanding of proxy data reduces technical uncertainty and helps reducing the number of experimental dead 128


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ends that are explored. Ultimately, the proxy data will reduce uncertainty and provide more unambiguous answers to extrapolate desired end-results back to define the optimal starting condition in innovation.

Scientific knowledge about process innovation therefore has three roles. Firstly, it allow patterns of behaviour to be understood and predicted (Nightingale, 1998:705). This allows engineers to achieve an understanding of the reasons why screening technology behaves as it does, and therefore an understanding of how potential changes to the technology will affect its behaviour. This we could call a ‘technical intuition’.

Secondly, scientific knowledge can be used to ‘screen alternatives’ before they are tested in the product development process. A given technological problem, and in turn its solution, will be bounded by various criteria; a certain density, temperature, activity etc. of the potential proteases. Scientific knowledge can then be used to perform approximate tests to ensure that potential screening designs meet biochemical screening principles. In other words, through abstraction they are reducing categories to end up with proxy data, which then determines potential screening designs. An avid engineer can then deploy his scientific knowledge to test the screening technology instead of testing it empirically. The result would be a drastic time compression and efficiency in the technological process innovation.

Thirdly, scientific knowledge about how the technological system works can be used to understand how things function. These functions can then be extrapolated to novel situations were a similar problem needs to be solved. For example, when AMSA was implemented in ADU a similar function in MB was extrapolated, engineering expertise added, and the correlation between AMSA in ADU and full scale wash is achieved. The same process could potentially be based on proxy data in developing a new form of screening technology.

Finally, Nightingale’s cognitive model of innovation provides a theoretical explanation of what is well known empirically, that while scientific knowledge does not directly produce technology it has a vital indirect role in the innovation process.

By applying the cognitive model of innovation we can conclude two things. Firstly in accordance with Nightingale (1998:705), “scientific knowledge cannot be directly applied to

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produce technology because it answers the wrong question. Innovation progresses from a known, desired end result to find the starting conditions that will achieve it, while scientific knowledge, in contrast, can only be used to move the opposite direction, from known starting condition to an unknown end result”.

Secondly, we have argued how this ‘direction problem’ is overcome by following tacitly understood technological traditions based on embodied and embedded conceptions of similarity. These technological traditions provide a mechanism that guides innovation and allow problems that are initially nebulous and very general to be resolved to specific problems and solved (Nightingale, 1998:705).

Conclusively, the cognitive model of innovation (Nightingale, 1998) in combination with an ‘abstract innovation space’ can enable the chemical engineers to identify the exact cause and effect relationship and close the gab between desired end-results and unknown starting conditions. Ultimately, the abstract innovation space would enable the engineers to translate knowledge about cause and effect relationships, i.e. proxy data, into more precise starting conditions in the innovation of new and more efficient screening technologies.

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10 DISCUSSION In this chapter we would like to put three areas under discussion. It is our impression that some aspects of considerations in the analysis, both theoretical and practical, need a further examination and evaluation before the conclusion rounds off the thesis. Consequently, it is relevant to reconsider the following three issues.

The competence-based vs. knowledge-based perspective In this thesis we have employed two main theoretical perspectives, namely on one side the competence-based view and on the other side the knowledge-based view, or rather what we have labelled the cognitive view. The theoretical perspectives are closely related and to a large extent overlapping. Nevertheless, they are distinct in their basic assumption of how to obtain sustainable competitive advantage. The competence-based view of the firm emphasises that it is the development and deployment of unique and idiosyncratic skills, technologies and resources that is the foundation for achieving the competitiveness, growth and survival of an organisation. Further, it is believed that competitive advantage is obtained from value retention through an imitation blockage. The knowledge-based view agrees with respect to the constituents generating competencies, however focus is on the intangible elements and it is believed to be the cognitive processes creating new knowledge and competencies. Moreover it is emphasised that sustainable competitive advantage emerges from value creation and value exploitation rather than value retention (Spender & Grant, 1996; Nonaka & Takeuchi, 1995). In that respect, it is believed that companies can sustain competitive as long as they create and exploit knowledge faster and more efficient than competitors.

It has foremost been our goal to apply the relevant theoretical aspects from each line of thinking based on their complementing characteristics. In this respect it is an interesting consideration if we would have reached a different conclusion with regards to the core competence in the protease department. We determined the core competence in the protease department to be the ability to â&#x20AC;&#x153;â&#x20AC;Śto screen under as realistic conditions as possible while maintaining high through-put and high quality assaysâ&#x20AC;?. The core competence was determined from a competence-based perspective and validated in cooperation with the department. However from a cognitive perspective, the core competence rather seems to be the ability create and exploit knowledge in process innovation, i.e. in the innovation of new and better

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screening technologies. This is because, it is in the cognitive processes value is created, which enables the department to subsequently screen under as realistic conditions as possible etc. From a competitive or customer viewpoint, the core competence of the protease department is possibly considered to be the ability to produce new and revolutionary enzymes faster and more economically, which again is a result of the time compression advantage referring back to the cognitive processes. What crystallises is that the core competence depends on the theoretical perspective and on the view of the analyst. This poses another important consideration, which is how a company perceive its core competence. The protease department could potentially get lost in translation of their core competence. Depending on the spectacles they look through, it could be one the above, and it is crucial to a company or department that they are aware of the core competence in order to follow the common goal.

Tangible vs. Intangible constituents The division between the competence-based view and the cognitive view poses yet another consideration. Because of the desire and necessity to first clarify the process innovation by means of the framework, the use of tangible constituents of competence was required, without which the process innovation could not have been visualised. However, the subsequent cognitive view in fact focuses on primarily the intangibles in competence development. This may appear somewhat contradictory. Nevertheless, the process illustration in that sense essentially works to give the reader an abstract comprehension of the entire process development, whereas the cognitive processes are deducted from the interaction of physical constituents. As such we imagine an underlying stream of intangible processes in terms of learning, cultural factors, organisational mechanisms that are largely tacit and inexpressible in the process illustration. In effect this promotes the abstract understanding of the process innovation as a whole and the underlying understanding of the cognitive processes that we are subsequently analysing.

Path dependency and the innovation trajectory Within the resource-based view of the firm there are a number of theoretical perspectives we have left aside. One such major school of thought related to the competence-based view of the firm is the evolutionary theory of the firm (Nelson & Winter, 1982; Burgelman & Rosenbloom, 1989). This school of thought suggests that â&#x20AC;&#x153;history mattersâ&#x20AC;? and emphasises that, for example, previous investments and existing organisational routines to a large extent

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determine the range of behaviours available to a company. As such, companies can be said to develop their competencies in a â&#x20AC;&#x153;path dependentâ&#x20AC;? manner along a trajectory determined by previous decisions and actions (Dosi, 1882).

The perspective above is not explicitly involved in the thesis; however considerations have been made and incorporated in our way of thinking. History matters especially in the case of Novozymes which as mentioned is highly influenced from the national system of innovation. This path dependency could also reflect the current way of innovating in the sense that traditional procedures of innovation transpire throughout the organisation. For example, talking about the protease department following codification or say replication as innovation trajectory, it could prove difficult to turn the department towards a more abstract innovation space. It is a matter of path dependency and tacit background knowledge, and it is easier said than done, to change the way people perceive and comprehend process innovation. Further, the very tacit nature of knowledge and the way a shared tradition of technological understanding co-evolves with a technological trajectory, makes it even more difficult to redirect or shift the current innovation trajectory (Nightingale, 1998:706). Finally, since Novozymes already gorge in success and experience a low pressure from competitors, the incentive to take on a new trajectory is weak.

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PART IV 11 CONCLUSION The motivation for this thesis emerged from the huge innovative challenges facing a technology intensive company such as Novozymes, combined with the exceptional competence development going on during the process innovation in the protease department from 1999 to 2004. The point of departure for this thesis was therefore to analyse the process innovation from 1999 to 2004 to understand the cognitive dimension accelerating such major competence development.

The detergent industry is by far the most advanced industry for enzymes, and Novozymes has managed to reduce the product development process from 10 to 5 months. Continuous process innovation is a prerequisite for successful product development, and therefore it made sense to investigate the maiden ground of how knowledge is created and applied in process innovation. The time reduction in product development is a major advantage relative to competitors and must be a result of considerable competence advancement. For that reason, it was our objective to come to and understanding how Novozymes has achieved such competitive advantage.

The motivation and theme led us to formulate three research questions: 1. What are the constituents of competencies leading to successful process innovation in the protease discovery department? 2. Why have the protease discovery department hitherto succeeded in fostering knowledge leading to competence development? 3. How can the protease discovery department exploit current knowledge and competencies in future advanced process innovations?

To answer research question 1 it was necessary to answer two sub-questions.

A. What constituents of competencies should our working definition include? B. How should a methodology and framework to identify constituents of competencies be designed and applied?

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We commenced this thesis by clarifying the terminological confusion of competence within the resource-based theory of the firm. Subsequently, to answer sub-question 1A, the focus on constituents of competencies within the competence-based view of the firm was determined, and based on this the working definition of competence was derived. The literature offer various definitions of what competencies consist of, but common for most is the dual focus on tangible and intangible elements. Even though we went along with the argument, that a competence consists of both tangible and intangible elements, we decided to only include tangible elements in the working definition. This was due to our focus on the identification and visualisation of constituents to competencies in the process illustration. In our opinion, it would be impossible to convincingly identify and visualise intangible constituents, and therefore well aware of the consequence, we focused on tangible constituents.

Synthesising a discussion based on input from the theory we proposed a working definition of competence that divided into three main constituents: human skill, technology, and organisation. We argued that human skill is the basic constituent of a competence and presented three types of knowledge to better capture the underlying relationships of skillâ&#x20AC;&#x2122;s influence on competencies. Specific knowledge was characterised as knowledge possessed by an individual or team about a specific technology or scientific discipline. Integrative knowledge was knowledge integrating different areas or bodies of specific knowledge. Finally deployment knowledge concerns the ability to mobilise and apply the companyâ&#x20AC;&#x2122;s stock of specific and integrative knowledge. Regarding the second constituent, technology, we found it valuable to distinguish between generic and customized technologies, and concluded that AMSA and full scale wash are the primary customised technologies playing significant roles in the sustainable competitive advantage in product development. Organisation was the last constituent in the working definition. We defined organisation as creating the foundation and opportunity for skills and technologies to be integrated and coordinated in a fruitful manner. Therefore organisation is setting the premises for learning, which is a prerequisite for competence development. The working definition gave us a better foundation for understanding the competence development in the protease discovery department, because it enabled us to identify constituents of competencies and distinguish between them.

The working definition was used to determine which constituents to identify and visualise in the process illustration. Sub-question 1B concerns the development of the methodology and

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the framework to identify and visualise the constituents in the competence development. Since none of the existing frameworks for competence identification met our needs, we developed a new framework based on valuable input from four different methodologies. By means of a thorough analysis of the methodologies we decided to display the empirical findings, i.e. the constituents of the competence development, in a process illustration, to present the findings in a clear and comprehensive way. Also, during the analysis, it became apparent that the best approach to identify the constituents and draw up the process illustration was by means of a workshop involving key personnel from the two departments MB and ADU. The methodology developed consists of five phases and worked to identify the constituents of the competence development during the workshop.

To answer research question 1, successful application of the framework to the empirical case was critical. To all intents and purposes, answering research question 1 was an important foundation for addressing research question 2 and 3. The process illustration answered research question 1 by visualising a detailed presentation of key constituents and the linkages between them. By codifying and visualising the competence development a very complex process innovation successfully crystallised, and it became obvious what skills and technologies had the most profound impact on the competence development. The 6 activities each offered a detailed presentation of specific parts of the competence development and contributed to the codification of the process innovation. Based on the process illustration and the six activities presented it was possible to point out two key technologies leading the process development.

First of all a better understanding of full scale tests results had a profound impact because the results were used to benchmark on existing and new screening technologies. In addition, the development of AMSA was recognized as the central single technology accelerating the entire development process along with highly improved communication through better integration and coordination activities.

By answering the two sub-questions and research question 1, we established the foundation necessary to understand the competence development, which again was a prerequisite for answering research question 2 and 3 from a cognitive perspective.

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Research question 2 led to the discussion of the cognitive aspect of the competence development in the protease discovery department. Several dimensions were discussed to elucidate the research question. Boisot (1995, 1998) taught us that codification and abstraction are the cognitive processes through which the protease discovery department manages to comprehend the technological system, and through which they come to economise on data processing. In the protease department, economising on data processing basically means reducing the time necessary to comprehend a given technology or system of technologies, meaning that employees use less resources to comprehend the interwoven technological complexity. Hence it could be argued that the clarity of technological interrelationships provides the basis for faster decision-making and more efficient problem solving, which ultimately translates into a ‘time compression advantage’. The time compression advantage is a result of more efficient use of knowledge resources, but to actually lead to a competitive advantage those knowledge resources must be translated into innovative actions.

In combination, three building blocks in the cognitive process of translating an unknown starting condition to a known, essentially explain how knowledge is applied to innovation.

The learning experience and the tacit background knowledge developed over time provide engineers with the ability to translate abstract knowledge about technological systems, and thereby extrapolate desired future end-results back to identify better starting conditions for innovation. When designers and engineers imagine a functional solution to e.g. a new screening technology, they start with a given background of tacit design knowledge. They cannot just apply scientific knowledge or pattern and expect a result to simply pop out. Instead, the technologist must ‘see’ how the problem they face relates to similar problems they have faced in the past. Technologist can then exploit tacit knowledge in innovation, by assuming that similar problems will have similar solutions, and by taking the problem with an unknown solution and compare it to a similar problem with a known solution, they assume that the solutions will be alike. This way the cognitive process on one hand work as value creating and on the other hand as value exploiting, whereby knowledge is translated into new competences, in terms of developing new and better screening technologies.

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In practice, the full scale wash in China combined with enhanced statistical software, provided vastly improved information flows that enabled the protease department to benchmark full scale wash against internal screening technologies. Moreover, the information efficiency and the abstract comprehension of the technological system enabled the engineers to replicate data into miniature principles and model better screening technologies. Altogether, this tacit background knowledge enabled engineers to identify better starting conditions for innovation, whereby screening technologies and the entire screening process was optimised.

Research question 3 examined how the protease discovery department can exploit current knowledge and competencies in future process development. To answer the question we employed a cognitive perspective to some of the issues that characterise the current process innovation. The result was a hypothetical suggestion of how to achieve future advanced process innovations. We emphasised that the interpretation was from a pure theoretical viewpoint that may not practically be realistic.

By returning to the I-space theory we could reveal that the process innovation currently evolves within a ‘codified space’ whereas the future opportunities may reside in a more ‘abstract innovation space’. Further we suggested that an abstract innovation space helps to identify the optimal starting conditions for process innovation. It was argued that the AMSA technology is a miniature replication of full scale wash that seeks to find the optimal balance between high through-put, reproducibility and application. This is achieved by codifying and replicating the biochemical processes under which the enzyme must function (full scale wash) and by codifying and replicating the principles under which such processes must operate, i.e. the new screening technology. In practise it means codifying the technical and biochemical principles and then replicating them into a miniature technology. The lack of abstraction is a problem because they do not fully comprehend data from full scale wash to perfectly exploit the innovation opportunities in screening technologies. It was our evaluation that the protease discovery department can benefit from an increased level of abstraction, through which they will more efficiently economise on data processing and identify a more favourable starting condition for further innovation. We argued that by reaching a more codified and abstract level of understanding the protease discovery department may be able to extract proxy data that determine the essential screening

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parameters. We suggested that if it is possible to extract the essential proxy data, the screening technology can be significantly simplified and uncertainty of testing on a large number of categories in the screening process can be reduced considerably. Conclusively, by obtaining an ‘abstract innovation space’7 the department would not only economise on data processing, but more importantly, they would be able to deduct single cause and effect relationships from test data in full scale and other screening technologies, whereby an overall complete comprehension can be achieved. Ultimately, the proxy data would provide the engineers with clear and unambiguous answers to extrapolate desired end-results back to define the optimal starting condition for new innovation purposes.

HUSK COMPETTITIVE ADVANTAGE SNAK…ER DEN SUSTAINABLE?

7

Boisot (1998) does not exactly use the phrase ‘abstract innovation space’, however we believe it corresponds well with a SLC that fully exploits codification and abstraction in the learning process.

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12 LOOKING BEYOND THE SCOPE OF OUR THESIS The purpose of this chapter is to look beyond the scope of this thesis and suggest how our findings can be useful elsewhere. The analysis of the process innovation provided a number of interesting findings. From a cognitive perspective we have succeeded in explaining why the protease discovery department has managed to so successfully process innovate and also offered our suggestions to how they can improve innovation in the future. Since protease is the most advanced discovery department in Novozymes, our findings, i.e. explaining their success in process innovation, offers attractive insights to other discovery departments within Novozymes.

During our work we discussed this possibility with Steen Skjold Jørgensen, International Strategy Group and he confirmed that our findings could be valuable in the development of other discovery departments, for example bakery. Thus, below we will try to elaborate on our findings in relation to the bakery discovery department.

The trajectory of process innovations in bakery is very different from the trajectory we outlined in the discussion above. In bakery they do not have the same opportunity to develop screening technologies that replicate the full scale test in miniature versions like the AMSA. The nature of the bakery industry makes it impossible to screen by baking a â&#x20AC;&#x2DC;mini bunsâ&#x20AC;&#x2122;. During the cognitive analysis we argued that developing miniature versions of the full scale test could be achieved with a codified level of understanding. However, this is not an option for bakery. Following our reasoning bakery must develop a codified and abstract level of understanding in order to develop more advanced screening technologies than miniature versions.

Today, bakery does not have high through-put screening technologies and to be successful long term, they must develop a codified and abstract level of understanding to create the necessary foundation to process innovate. In our opinion, a codified and abstract level of understanding is best achieved through in dept analysis of the processes of rising of dough and baking of bread. If these processes are understood we have reason to believe that the bakery department would be able to develop advanced screening technologies based on proxy

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data. The intriguing question is however how the necessary understanding is obtained and used to develop new screening technologies.

With point of departure in this thesis we believe that the situation we have just outlined offers another valuable piece of insight to Novozymes. Therefore we believe that an indept analysis of process innovation in bakery would be an interesting title for another thesis.

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1 3 B I BLI O G R A P H Y (Nelson & Winter, 1982; Burgelman & Rosenbloom, 1989). Grant. 1996a; Spender & Grant, 1996; (Andersen, 1999:163; Maaløe, 1999:33; Yin, 1994:8-91). (Hill, Charles, W.L. (1998), Irwin McGraw-Hill) (Tufte, E.R. “The Visual display of quantitative information”, Cheshire, CT: Graphics Press, 1983) and (Tufte, E.R. “Envisioning information” Cheshire, CT: Graphics Press, 1983) (Lloyd, 2000, 2002) 1

Popper, K. R., (1993) Realism and the Aim of Science, London: Hutchinson.

Drucker (1993) (Weick, 1990).

Barney, J. (1991), “Firm resources and sustained competitive advantage”, Journal of Management, 17 Barrow, J.D., (1991), “Theories of Everything”, Oxford University Press, Oxford. Cohen, W. M. & Livithal, D.A., (1990), Absorptive Capacity: A New Perspective on Learning and Innovation, Administrative Science Quarterly, vol. 35, pp. 128-152. Diericks and Cool (1989), “Asset Stock Accumulation and Sustainability of Competitive Advantage”, Management Science 35/12 Dorsi, G., Nelson, R.R., Winter, S.G. (2000), “Introduction: The Nature and Dynamics of Organizational Capabilities“, The Nature and Dynamics of Organizational Capabilities Dorsi, G., Teece, D. (1993), “Organizational Competencies and the Boundaries of the Firm“ Drejer, A. (2002), “Strategic Management and Core Competencies“ Drucker, P. (1993), Post-Capitalist Society. London: Butterworth Heinemann. Durand, T. (1998), “The Alchemy of Competence“, Strategic Flexibility Garvis, D.M. and Bogner, W.C. (1998), “Structure Decisions and the Multinational Enterprise: A Dynamic Competence Perspective” Strategic Flexibility Grant (1991), “The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation”, California Management Review, vol. 33 Griffith, Dorothy and Boisot, Max (2000), ”Are There Any Competencies Out There? Identifying and Using Technical Competencies”, Series on Technology Management –Vol. 3 Hamel (1994), “The concept of core competence”. In “Competence-based Competition” ed. Hamel, G. and Heene, A., Chichester: Wiley Hamel, G & Prahalad, C.K.(1993), “Strategy as Stretch and leverage”, Harvard Business Review, March/April

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Henderson, R.M. & Clark, K.B. (1990), ”Architectural Innovation: The Recognition of Existing Product Technologies and the Failure of Established Firms”, Administrative Science Quarterly, vol. 35. pp. 9-30. Henderson, R. and Cockburn, I. (2000), “Measuring Competence? Exploring Firm Effects in Drug Discovery“, The Nature and Dynamics of Organizational Capabilities Herderson and Cockburn (1995), “Measuring Competence? Exploring firm effects in pharmaceutical research”, Strategic Management Journal, vol. 15. Special Issue, pp. 63-84. Klavans, R. (1994), “The Measurement of a Competitor’s Core Competence”, Competence Based Competition Klein, J. A. and Hiscocks, P. G. (1994), “Competence-based Competition: A Practical Toolkit”, Competence Based Competition – In the book: Competence based competition. Kogut & Zander (1992), Leonard-Barton (1992), “Core Capabilities and Core Rigidities, A Paradox in Managing New Product Development”, Strategic Management Journal, vol. 13 Levitt, B. and March, James G. (1996), “Organizational Learning”. In: M.D. Cohen and L. Sproull, Organizational Learning. Thousand Oaks: Sage. (pp. 516-40) Lewis, M. A. and Gregory, M. J., “Developing and applying a process approach to competence analysis.”, In Dynamics of competence-based competition (1996). Lippman and Rumelt (1982), “Uncertain Imitability: an analysis of interfirm differences in efficiency under competition”, Bell Journal of Economics, 13 Miyazaki, K., “Building Competence in the Firm”, Chapter 2 & 7 Emperical study of Japanese hichtec companies. Narduzzo, A., Rocco, E., Warglein, M (2000), “Talking about Routine in the Field: The Emergence of Organizational Capabilities in a New Cellular Phone Network Company“, The Nature and Dynamics of Organizational Capabilities. Nielsen, A. P., (1998), “Competence Development though Commercialisation – seen in a Manufacturing context”, Department of Industrial Management and Engineering, Technical University of Denmark. Nonaka, I. (1994), “A dynamic theory of organisational knowledge creation“. Organisational Science 5(1) Nonaka and Takeuchi (1995), “The Knowledge-Creating Company”, New York: Oxford University Press

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Nonaka et al. (1996), “A theory of organisational knowledge creation”, IJTM, Special Publication on Unlearning and Learning. Vol. 11, No 7/8 Pisano, G. (2000), “In Search of Dynamic Capability: The Origins of R&D Competence in Biopharmaceuticals“, The Nature and Dynamics of Organizational Capabilities Prahalad, C.K. and Hamel, G. (1998), “The Core Competence of the Corporation“, The Strategy Reader Prahalad, C.K. and Hamel, G. (1990), “The Core Competence of the Corporation”, Harvard Business Review, May/June Præst, M. (1998), “An Empirical Model of Firm Behaviour: A Dynamic Approach to Competence Accumulation and Strategic Behaviour”, DRUID Working Paper No. 98-1 Skyes, P., (1986), “A guidebook to mechanism. In: Organic Chemistry”, 6th Ed. Longman Scientific and Technical. Snyder and Ebeling, (1992), “Targeting a company’s real core competences”, Journal of business strategy, Vol. 13. issue 6. Nov/Dec. Turro, N.J., (1986), “Geometric and topological thinking in organic chemistry” Angew Chem. Int. Ed. 25, 882-901, English. Tidd, J., ”The Competence Cycle: Translating Knowledge Into New Processes, Products and Services”, Series on Technology Management –Vol. 3 Wernerfelt, B. (1984), “A Resource-based View of the Firm”, Strategic Management Journal, 5 Zollo, M. and Winter, S. (2002), “Deliberate Learning and the Evolution of Dynamic Capabilities”, Organization Science Vol. 13 no. 3 May/June 2002 Von Krogh, G., Roos, J. & Slocum, K. (1994), “An essay on Corporate Epistemology” Strategic Management Journal, vol. 15. pp.: 53-77 Weick, Karl E. & Westley, Frances (1996), “Organization Learning: Affirming an Oxymoron”. In: S.R. Clegg, C. Hardy and W.R. Nord (eds.), Handbook of Organization Studies. London: Sage. (pp. 440-58)

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1 4 A P P EN D I X 14.1 List of interviewees 14.2 Individual interviewees Name:

Kirsten Værver Jokumsen (KVJO), Head of ADU

Name:

Niels Henrik Sørensen (NHS), Creativity manager

Name:

Steffen Ernst (SFFE), Head of MB

Name:

Steen Skjold Jørgensen, Head of R&D bakery ISG

14.3 Workshop participants Name:

Gernot (GEAB), Automation expert

Name:

Jürgen (JUKN), Assay expert

Name:

Henriette, Chemist

Name:

Kirsten Værver Jokumsen (KVJO), Head of ADU

Name:

Stefan (SMIN), Protein engineer

Name:

Steffen Ernst (SFFE), Head of MB

Name:

Vibeke Skovgaard Nielsen (VISN), Chemical engineer

14.4 Process illustration only Name:

Martin Schülein (MAS), Chemical engineer

Name:

Søren Kjærulf (SNK), Technical engineer

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14.5 Technology description FITC A high through-put screening technology based on a chemical that illuminates when interacting with the most active proteases. Low reliability caused MB to abandon the technology in 2000.

Microtit A screening technology that tests proteases on stains in very small scale (250 microlitre). The technology was abandoned in favour of AMSA due to limitations regarding capacity of tests and the quality of the tests.

AMSA The screening technology washes small concentrations of proteases and cloths under pressure. The capacity is 396 small wells with different proteases and cloths per wash. The advantage of AMSA is a dramatic increase in both capacity and quality.

Mini wash The screening technology washes medium concentrations of proteases testing different detergents and stains. Concentration and capacity is between AMSA and full scale wash.

Full scale wash The screening technology tests the most valuable proteases in full size washing machines using different detergents, stains, and water qualities.

AST A test to determine the concentration of molecules in a given microtit. This is important to extract reliable test results.

Fermentation The process of growing enzymes based on micro organisms.

Purification The process of separating the proteases from the mush of micro organisms and manure.

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Innovation funnel

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14.6 Process illustration Please to refer to A3 illustration on the next page.

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4 DISCUSSION OF FRAMEWORKS TO IDENTIFY COMPETENCIES .............................. 42 7 PRESENTATION AND EXPLANATION OF THE EMPIRICAL FINDI...

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